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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way .
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but would n't you put a line over top of the number if it keeps repeating ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people .
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is n't mount everest to much for 2^63 pile of rice ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: yeah , they have things to do . they have to go home and count grains of rice . : right .
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did brit count out all of the rice ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
|
so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say .
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what is the exponent of zero ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
|
: when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ...
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is there a way to divide exponents ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: yeah , they have things to do . they have to go home and count grains of rice . : right .
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how many grains of rice are there on the board after all ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less .
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did britt really count the grains of rice ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks .
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can this `` branching '' be infinite ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 .
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please i want to know how to answer quickly exponents like this : cube root of 1/8 is equal to what ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again .
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if a savings account started at $ 100 and earned 7 % interest per year , how much would be in the account at the end of 12 years ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 .
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what time does it take for humans to poop between each hour of the day ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less .
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is brit really count the grains of rice ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice .
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was n't this made from some chinese dude who made a chessboard for the emperor and ended up rich ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times .
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what can the binary tree also apply to besides exponents ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks .
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isi n't exponential growth a number times a number like 1squared 1 x 1 = 1 ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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: junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice .
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is n't there an chinese legend that has a similar scenario ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways .
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what is 2^2^2 and 2^2^2^2^2^2^2^2^2^2^2^2^2 ?
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: hi sal . : hey britt . : how are you ? : good , looks like we have a game going on here . : not a game . yeah , kind of a challenge question for you . what i did is , i put 1 grain of rice in the first square . : that 's right . : there 's 64 squares on the board . : yup . : and in each consecutive square i doubled the amount of rice . : mm hm . : how much rice do you think would be on this square ? : on that square ? let me think about it a little bit . actually , i 'm going to take some ... here you have 1 and we multiply that times 2 , so this is going to be 2 times 2 . no , no 2 times 1 , what am i doing ? now this is 2 times that one so this is 2 times 2 . now this is 2 times that . so this is ... okay , we 're starting to take a lot of 2 's here and multiplying them together . so this is 2 times 2 ... i 'm trying to write sideways . times 2 . this one is going to be 5 , 2 's multiplied together . this is going to be 6 , 2 's multiplied together . this is going to be 7 , 2 's multiplied together . 8 , 2 's multiplied together . 9 , 2 's . 10 , 11 , 12 , 13 . so all of this stuff multiplied together . 8,192 grains of rice is what we should see right over here . : and you know , i had fun last night and i was up late , but there you go . : did you really count out 8,192 grains of rice ? : more or less . : okay . let 's just say you did . : what if we just went , you know , 4 steps ahead . how much rice would be here ? :4 steps ahead , so we 're going to multiple by 2 , then multiple by 2 again , then multiply by 2 again , the multiply by 2 again . so it 's this number times ... let 's see , 2 times 2 is 4 . times 2 is 8 , times 2 is 16 . so it 's going to get us like 120 , like 130,000 or around there . :131,672 . : you had a lot of time last night . we 're not even halfway across the board yet . : we 're not . : this is a lot of ... that 's a lot of rice , there . you could throw a party . : what about the last square ? this is 63 steps . : we 're going to take 2 times 2 and we 're going to do 63 of those . so this is going to be a huge number . and actually , it would be neat if there was a notation for that . : i did n't count this one out but it is the size of mount everest , the pile of rice . and it would feed 485 trillion people . : but i have one question . i mean , you know , this was a little bit of a pain for me to write all of these 2 's . : so was this . : if i were the mathematical community i would want some type of notation . : you kind of got on it here . i like this dot , dot , dot and the 63 . this i understand this . : yeah , you could understand this but this is still a little bit ... this is a little bit too much . what if , instead , we just wrote ... : mathematicians love being efficient , right ? they 're lazy . : yeah , they have things to do . they have to go home and count grains of rice . : right . ( laughter ) : yeah . so that is , take 63 , 2 's and multiply them all together . : this is the first square on our board . we have 1 grain of rice . and when we double it we have 2 grains of rice . : yup . : and we double it again we have 4 . i 'm thinking this is similar to what we were doing , it 's just represented differently . : yeah , well , i mean , this one , the one you were making , right , every time you were kind of adding these popsicle sticks , you 're kind of branching out . 1 popsicle stick now becomes 2 popsicles sticks . then you keep doing that . 1 popsicle stick becomes 2 but now you have 2 of them . so here you have 1 , now you have 1 times 2 . now each of these 2 branch into 2 , so now you have 2 times 2 , or you have 4 popsicle sticks . every stage , every branch , you 're multiplying by 2 again . : i basically just continue splitting just like a tree does . : yup . : now i can really see what 2 to the power of 3 looks like . : and that 's what we have here . 1 times 2 times 2 times 2 , which is 8 . this is 2 to the third power . : when i see 2 to the power of something , let 's just say n. n could also be number of steps up this tree . i could think about it that way . : yeah , you could view it ... i guess one way to think about it is how many times you 've branched . but that one , that tree there , is actually even more interesting . : i do n't think this counts because , again , this branches 4 times at each branch . : well i guess why not ? it 's different . it 's not going to be 2 anymore . so the first one where you have n't branched yet , this is going to be 4 to the 0 power . you 've had no branches yet . this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this . : and now each of those . so now you 've branched twice . so now this is 4 to the second power . so yeah , the base , or what 's called the base when you take an exponent , this 4 right over here . this is how many new branches each of the branches turn into at each of these , i guess , junctions you could say . : let 's call them junctions . : junctions . you have n't branched yet . here you 've branched once , and here you 've branched twice . : this is , this is interesting . this is also why when i look at a tree there 's thousands of leaves but just 1 trunk . and when you actually go up and you look inside the tree it only branches 3 or 4 times . : and that shows the power of exponential growth . : yes . ( laughs )
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this , you branched once so now this is 4 to the first power . you have 4 branches now . : oh , i like this .
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i was confused when u did the model of a tree on the paper wouldnt it be 4 to the power of 0 then 4 to the power of 4 because it had 4 branches ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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but what i would like to ask - what are the algorithms considered to implement the biggest amount of advanced techniques in computational and/or natural sciences ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given .
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is there a way to measure the complexity of an algorithm ( despite the length of code it takes to describe ) ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm .
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can you try to get your presenters to be more considerate of your international presence ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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would dna be considered an algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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could you explain min-max algorithm and how to implement in a tic-tac-toe game ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ?
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is it possible for amateurs to create complex algorithms to use in programs or is it something that only professionals can accomplish after sitting and working on them for a really long time ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given .
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is there a way to measure efficiency ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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is tere a way to solve a math problem with algorithms ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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could dna be an algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently .
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do we need to learn algorithm to be a good programmer ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what programming language and/or algorithms is used in machine learning and ai ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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how do you know if you 've written an incorrect algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs .
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how does computer science affect others in real life ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors .
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if i want to build a social networking site , what programming language should i study ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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what kind of algorithm is used for facial recognition technology ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store .
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what is the difference between a traditional algorithm and a fractal one ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves .
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what happens if that checkers program plays against itself ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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how can we find the average of three numbers ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ?
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at which grade of math is good for computing programming ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science .
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how does a computer processor process huge algorithm really fast ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time .
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if there were about 200 delivery locations and almost a million different routes , how does the algorithm function so quickly to give an answer within seconds ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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if you make an algorithm that is not efficient , is it not considered an algorithm anymore ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how long it will take to learn algorithms ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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where are these algorithms to be found ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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i'am a student going to complete my degree in ece department , but i was very much interested in programming side i am planning to develop apps in android this algorithm can help me in any way ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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question for the audience : what are some examples of algorithms you can think of ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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why do algorithm need to be invented ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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dose algorithms mean following instructions like how to do something step-by-step .but written in programming language ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science .
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if i threw my computer in the fishtank would my router be affected ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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how much prior mathematical knowledge is required ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how are algorithms used in rendering ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store .
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one question : how do i know if i 've inputted an incorrect algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science .
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what is the difference between the algorithm and procession in the computer ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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what does https : // mean ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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my question is , how does algorithms work ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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is there a way to solve a math problem with algorithms ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes .
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in physics , algorithms simulate climate what does this mean ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task .
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are innate algorithms done subconsciously by the human brain less complex than ones that can be handled by a machine that operates solely on a basis of 0 's and 1 's ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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or can a html file also be considered as an algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer .
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how does someone know how to create something so complex ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how data analysis and algorithms relate with each other ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given .
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how do you measure efficiency ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ?
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does anyone know how , the video compression algorithms work ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ?
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when are we going to need to use algorithms in making a game ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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what is the pi calculus ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science .
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what is diffrence between a computer program and an algorithm furthermore is the `` if and else function an algorithm or not `` ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store .
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are the steps in algorithm sequential along a vector , a scalar or both ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs .
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will computer science cover ethical hacking/hacking lessons ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science .
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what is the difference between an algorithm and a computer program ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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are there any qualifications to learn algorithms except javascript and logarithms , are there some more qualifications ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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can anything be done without an algorithm or procedure ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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what does the subject refer to ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science .
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what is the most useful algorithm for a computer programmer ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms .
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how does google hangouts transmit and receive data so quickly ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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i have a quick curiosity would dna be considered an algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques .
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if you own a $ 1,000,000 home why should you never buy house insurance ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs .
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is it possible to take ap computer science without any background knowledge in programming ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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does anybody know what algorithm actually is on earth ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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can the definition of algorithms be applied in computer architecture ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time .
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do the animators have to re-draw the point of view every time when the character moves and looks around the environment again ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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am i right in thinking that an algorithm can also consist of a set of many more algorithms within them ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ?
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can you use small algorithms to make a bigger algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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what is the types of os ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer .
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how do people make apps ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques .
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what algorithm is used in google search querry ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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can algorithms help my website to gain a higher rank on search engines ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task .
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is the concept like probability ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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is there an algorithm for creating an algorithm ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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who made the algorithms first ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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so if an unbeatable algorithm can be applied to a chess game , whats stopping people from putting a similar algorithm inside a computer virus or a machine or ai robot ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ?
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why would you want a good router instead of a great router ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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so , why should i care about algorithms at all ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ?
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why does the narrator use poor grammar ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer .
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how can i think more systematically when writing programs contains so many classes ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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will it be helpful by learning algorithm to change the way of thinking ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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which sort of algorithm is used for computing signals to flight and railways ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science .
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what exactly is computer scince any way ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs .
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can someone please tell me what sections for computer science go over the entire course material for cs 160 ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently .
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it was good to learn about the algorithms because is a process or set rules to be followed in calculations , it helps you a lot , how you prove is the algorithm is right or wrong ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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what is an algorithm ? one definition might be a set of steps to accomplish a task .
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how can an algorithm influence the way of life / can it change the way we think ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others .
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can anyone tell me what is a sentinal loop ?
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what is an algorithm ? one definition might be a set of steps to accomplish a task . you might have an algorithm for getting from home to school , for making a grilled cheese sandwich , or for finding what you 're looking for in a grocery store . in computer science , an algorithm is a set of steps for a computer program to accomplish a task . algorithms put the science in computer science . and finding good algorithms and knowing when to apply them will allow you to write interesting and important programs . let 's talk about a few famous algorithms . how does google hangouts transmit live video across the internet so quickly ? they use audio and video compression algorithms . how does google maps figure out how to get from dallas , texas to orlando , florida so that you can get to disney world ? they use a route finding algorithm . how does pixar color a 3d model of a character based on the lighting in a virtual room ? they use a rendering algorithm . how does nasa choose how to arrange the solar panels on the international space station and when to rearrange them ? they use an optimization and a scheduling algorithm . those algorithms are more complex than our everyday algorithms like making a grilled cheese sandwich . but they boil down to the same thing , a set of steps to accomplish a task . if you know something about existing algorithms , you can save yourself some effort and make your programs faster by applying the right one . for example , let 's say that you 're writing a game and you want the user to be able to play against the computer . well , you could look at checkers games for inspiration . computer scientists have figured out how to write checkers programs that never lose by using the minimax search algorithm to search through the huge tree of possible moves . if your game is similar to checkers , then you might be able to use algorithms based on these techniques . if not , then knowing the limitations of those algorithms might lead you to redesign your game if it requires having a skilled computer player . it 's also important to know how to design new algorithms as well as how to analyze their correctness and efficiency . in the biological sciences , new algorithms are continually being designed with purposes like designing the molecular structures that are the core of disease fighting drugs . in physics , algorithms simulate climate and weather patterns . in other algorithms , search and analyze the vast data about stars in the universe that 's collected by automated space telescopes . across all the sciences , and even on websites like khan academy , efficient algorithms are needed to analyze huge data sets or to select intelligently from a vast number of possible decisions . in just about any area you might be interested , new algorithms will allow massive computational power to be harnessed to do things that people really need and care about . now , not all algorithms are created equal . so what makes a good algorithm ? the two most important criteria are that it solves a problem and that it does so efficiently . most of the time , we want an algorithm to give us an answer that we know is always correct . sometimes we can live with an algorithm that does n't give us the correct answer or the best answer because the only perfect algorithms that we know for those problems take a really , really long time . for example , let 's say we want a program that would determine the most efficient route for a truck that delivers packages , starting and ending the day at a depot . it would take weeks to run going through all the possibilities . but if we 're okay with a program that would determine a route that 's good but maybe not the best , then it could run in seconds . in some case , good is good enough . how do you measure the efficiency of an algorithm ? we could time how long it takes to run the code , but that would only tell us about that particular implementation in a certain programming language on a particular computer and just for the input it was given . instead , computer scientists use a technique called asymptotic analysis , which allows algorithms to be compared independently of a particular programming language or hardware so that we can conclusively say that yes , some algorithms are more efficient than others . now you can learn about algorithms and asymptotic analysis on khan academy thanks to the contribution of two dartmouth college professors . tom cormen is the first author of the most popular college algorithms textbook in the world , plus the author of algorithms unlocked . devin balkcom designed dartmouth 's intro cs course and researches robotics . he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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he built the world 's first origami folding robot . tom and devin will teach you many of the algorithms that you would learn in apcs or cs 101 , like searching algorithms , sorting algorithms , recursive algorithms and my personal favorite , graph algorithms . there will be tons of interactive visualizations , quizzes and coding challenges to help you understand better along your learning journey .
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how many algorithms can you have ?
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