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1.1 Machine Learning Overview | Welcome to machine learning!--[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=y8JgiWcUnU8 | Welcome to machine learning. What is machine learning? You probably use it many times a day without even knowing it. Anytime you want to find out something like, how do I make a sushi roll, you can do a web search on Google, Bing, or Baidu to find out. And that works so well because their machine learning software has ... | 415 | 1.1 Machine Learning Overview | Welcome to machine learning!--[Machine Learning | Andrew Ng]: Welcome to machine learning. What is machine learning? You probably use it many times a day without even knowing it. Anytime you want to find out something like, how do I make a sushi roll, you can do a web search on Google, B... | [0.0028585297986865044, -0.0013303899904713035, 0.025299115106463432, -0.017389122396707535, -0.004723487421870232, -0.0010667236056178808, -0.0027845746371895075, 0.0138521334156394, -0.02544059418141842, -0.02927340380847454, 0.007961439900100231, 0.012585247866809368, -0.02544059418141842, 0.004774934612214565, 0.00... |
1.2 Machine Learning Overview | What is machine learning? --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=AISftYVyS50 | So, what is machine learning? In this video you learn the definition of what it is, and also get a sense of when you might want to apply it. Let's take a look together. Here's the definition of what is machine learning that is attributed to Arthur Samuel. He defined machine learning as the feeble study that gives compu... | 500 | 1.2 Machine Learning Overview | What is machine learning? --[Machine Learning | Andrew Ng]: So, what is machine learning? In this video you learn the definition of what it is, and also get a sense of when you might want to apply it. Let's take a look together. Here's the definition of what is machine learning that is ... | [-0.00987097155302763, 0.009807328693568707, 0.01583428494632244, -0.020760225132107735, -0.006392901763319969, 0.004108131397515535, -0.0011622734600678086, 0.004461348056793213, -0.03510527312755585, -0.022885888814926147, 0.011207466945052147, 0.02558433637022972, -0.023713242262601852, 0.007458915933966637, 0.00057... |
1.2 Machine Learning Overview | What is machine learning? --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=AISftYVyS50 | to spend a lot of time on in this specialization is practical advice for applying learning algorithms. This is something I feel pretty strongly about. Teaching about learning algorithms is like giving someone a set of tools and equally important or even more important than making sure you have great tools is making sur... | 397 | 1.2 Machine Learning Overview | What is machine learning? --[Machine Learning | Andrew Ng]: to spend a lot of time on in this specialization is practical advice for applying learning algorithms. This is something I feel pretty strongly about. Teaching about learning algorithms is like giving someone a set of tools and... | [0.009558671154081821, 0.005770251154899597, 0.04147188737988472, -0.031097544357180595, -0.0039286138489842415, -0.005158496089279652, 0.00041341251926496625, 0.009896410629153252, -0.02984854392707348, -0.024597646668553352, 0.007608702406287193, 0.02829366736114025, -0.02239277958869934, 0.008048401214182377, 0.0069... |
1.3 Machine Learning Overview | Applications of machine learning -- [Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=hHYcNPfbBXQ | In this class, you learn about the state of the art and also practice implementing machine learning algorithms yourself. You learn about the most important machine learning algorithms, some of which are exactly what's being used in large AI or large tech companies today, and you get a sense of what is the state of the ... | 500 | 1.3 Machine Learning Overview | Applications of machine learning -- [Machine Learning | Andrew Ng]: In this class, you learn about the state of the art and also practice implementing machine learning algorithms yourself. You learn about the most important machine learning algorithms, some of which are exactly what's b... | [-0.0030387723818421364, 0.005705186165869236, 0.02701706811785698, -0.017264502122998238, 0.001072555547580123, -0.00518712168559432, -0.006482283119112253, 0.02621406689286232, -0.03657535836100578, -0.029037518426775932, 0.0008701866026967764, 0.019103631377220154, -0.005154742393642664, 0.000887995061930269, 0.0048... |
1.3 Machine Learning Overview | Applications of machine learning -- [Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=hHYcNPfbBXQ | by McKinsey, AI and machine learning is estimated to create an additional 13 trillion US dollars of value annually by the year 2030. Even though machine learning is already creating tremendous amounts of value in the software industry, I think there could be even vastly greater value that has yet to be created outside ... | 201 | 1.3 Machine Learning Overview | Applications of machine learning -- [Machine Learning | Andrew Ng]: by McKinsey, AI and machine learning is estimated to create an additional 13 trillion US dollars of value annually by the year 2030. Even though machine learning is already creating tremendous amounts of value in the so... | [-0.006752273999154568, -0.01578589901328087, 0.032096244394779205, -0.02238083817064762, 0.008823845535516739, -0.0041464208625257015, -0.015143449418246746, 0.0030811347533017397, -0.03128335252404213, -0.029998451471328735, 0.006303214933723211, 0.020912382751703262, -0.005926267709583044, 0.0035596939269453287, -0.... |
1.4 Machine Learning Overview | Supervised learning part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=EZN_uM3J3kI | Machine learning is creating tremendous economic value today. I think 99% of the economic value created by machine learning today is through one type of machine learning, which is called supervised learning. Let's take a look at what that means. Supervised machine learning, or more commonly supervised learning, refers ... | 500 | 1.4 Machine Learning Overview | Supervised learning part 1 --[Machine Learning | Andrew Ng]: Machine learning is creating tremendous economic value today. I think 99% of the economic value created by machine learning today is through one type of machine learning, which is called supervised learning. Let's take a look a... | [-0.004761981312185526, 0.0003801513812504709, 0.02268962934613228, -0.022379696369171143, -0.00830360408872366, -0.006741028279066086, 0.007167185191065073, 0.02063632756471634, -0.03540976718068123, -0.04005875438451767, 0.012055076658725739, 0.025659814476966858, -0.030786611139774323, -0.0020726723596453667, -0.008... |
1.4 Machine Learning Overview | Supervised learning part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=EZN_uM3J3kI | Y pairs, it can then take a brand new Input X, something it's never seen before, and try to produce the appropriate corresponding output Y. Let's dive more deeply into one specific example. Say you want to predict housing prices based on the size of a house. You've collected some data and say you plot the data and it l... | 500 | 1.4 Machine Learning Overview | Supervised learning part 1 --[Machine Learning | Andrew Ng]: Y pairs, it can then take a brand new Input X, something it's never seen before, and try to produce the appropriate corresponding output Y. Let's dive more deeply into one specific example. Say you want to predict housing price... | [0.008950799703598022, -0.0010360615560784936, 0.020370785146951675, -0.01767011359333992, 0.00015563025954179466, -0.011104907840490341, 0.003719854634255171, 0.018325991928577423, -0.031044872477650642, -0.03037613444030285, 0.001381683279760182, 0.03451716527342796, -0.022788530215620995, -0.009291598573327065, -0.0... |
1.5 Machine Learning Overview | Supervised learning part 2 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=l16C3PKiHKg | So, supervised learning algorithms learn to predict input-output or x-to-y mappings, and in the last video you saw that regression algorithms, which is a type of supervised learning algorithm, learn to predict numbers out of infinitely many possible numbers. There's a second major type of supervised learning algorithm ... | 500 | 1.5 Machine Learning Overview | Supervised learning part 2 --[Machine Learning | Andrew Ng]: So, supervised learning algorithms learn to predict input-output or x-to-y mappings, and in the last video you saw that regression algorithms, which is a type of supervised learning algorithm, learn to predict numbers out of in... | [0.005782643333077431, 0.009697840549051762, 0.018906282261013985, -0.026788193732500076, 0.003097384702414274, 0.00946602039039135, -0.004372399765998125, 0.01573806256055832, -0.03861105814576149, -0.031965527683496475, 0.0011647390201687813, 0.04291262477636337, -0.02789578214287758, 0.007489102892577648, 0.00319397... |
1.5 Machine Learning Overview | Supervised learning part 2 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=l16C3PKiHKg | So to summarize, classification algorithms predict categories. Categories don't have to be numbers. It could be non-numeric. For example, it can predict whether a picture is that of a cat or a dog. And it can predict if a tumor is benign or malignant. Categories can also be numbers like zero or one or zero or one or tw... | 482 | 1.5 Machine Learning Overview | Supervised learning part 2 --[Machine Learning | Andrew Ng]: So to summarize, classification algorithms predict categories. Categories don't have to be numbers. It could be non-numeric. For example, it can predict whether a picture is that of a cat or a dog. And it can predict if a tumor... | [0.01154077798128128, 0.01629061996936798, 0.025103576481342316, -0.02418794482946396, -0.008088083006441593, 0.007064355071634054, -0.0075984736904501915, 0.01865600235760212, -0.035913120955228806, -0.02586660347878933, 0.001905977027490735, 0.043746862560510635, -0.0317927785217762, 0.00518858153373003, 0.0037292928... |
1.6 Machine Learning Overview | Unsupervised learning part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=yzAFnfHYH9E | After supervised learning, the most widely used form of machine learning is unsupervised learning. Let's take a look at what that means. We've talked about supervised learning, and this video is about unsupervised learning. But don't let the name unsupervised fool you. Unsupervised learning is, I think, just as super a... | 500 | 1.6 Machine Learning Overview | Unsupervised learning part 1 --[Machine Learning | Andrew Ng]: After supervised learning, the most widely used form of machine learning is unsupervised learning. Let's take a look at what that means. We've talked about supervised learning, and this video is about unsupervised learning. B... | [-0.006049607880413532, 0.011759386397898197, 0.03377778083086014, -0.02621176280081272, 0.001303214463405311, -0.001489960472099483, 0.0011196744162589312, 0.0060624317266047, -0.026622122153639793, -0.04367772117257118, -0.0025375012774020433, 0.02427537553012371, -0.023980429396033287, 0.004302370827645063, -0.01134... |
1.6 Machine Learning Overview | Unsupervised learning part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=yzAFnfHYH9E | clusters. Now what's cool is that this clustering algorithm figures out on its own which words suggest that certain articles are in the same group. What I mean is there isn't an employee at Google News who's telling the algorithm to find articles of the word panda and twins and zoo to put them into the same cluster. Th... | 500 | 1.6 Machine Learning Overview | Unsupervised learning part 1 --[Machine Learning | Andrew Ng]: clusters. Now what's cool is that this clustering algorithm figures out on its own which words suggest that certain articles are in the same group. What I mean is there isn't an employee at Google News who's telling the algor... | [-0.0021487483754754066, 0.009575237520039082, 0.026252381503582, -0.0341462716460228, -0.017904071137309074, 0.01107481773942709, 0.009497337974607944, 0.012308238074183464, -0.027654584497213364, -0.04889538511633873, 0.004131958354264498, 0.01800793781876564, -0.03557444363832474, -0.0033123830799013376, -0.00670591... |
1.6 Machine Learning Overview | Unsupervised learning part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=yzAFnfHYH9E | these classes, subscribe to the batch weekly newsletter or attend our PyLAI events. Let's visualize the deeplearning.ai community as this collection of people. Running clustering, that is market segmentation, found a few distinct groups of individuals. One group's primary motivation is seeking knowledge to grow their s... | 282 | 1.6 Machine Learning Overview | Unsupervised learning part 1 --[Machine Learning | Andrew Ng]: these classes, subscribe to the batch weekly newsletter or attend our PyLAI events. Let's visualize the deeplearning.ai community as this collection of people. Running clustering, that is market segmentation, found a few dist... | [-0.00571842584758997, 0.007974994368851185, 0.020797928795218468, -0.02667705900967121, -0.0010177994845435023, 0.019405150786042213, -0.0020439689978957176, 0.014543818309903145, -0.02738684043288231, -0.050407856702804565, -0.0030333101749420166, 0.017570432275533676, -0.020690791308879852, 0.006679309066385031, 0.0... |
1.7 Machine Learning Overview | Unsupervised learning part 2 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=u7Y_b04upmQ | In the last video, you saw what is unsupervised learning and one type of unsupervised learning called clustering. Let's give a slightly more formal definition of unsupervised learning and take a quick look at some other types of unsupervised learning other than clustering. Whereas in supervised learning, the data comes... | 500 | 1.7 Machine Learning Overview | Unsupervised learning part 2 --[Machine Learning | Andrew Ng]: In the last video, you saw what is unsupervised learning and one type of unsupervised learning called clustering. Let's give a slightly more formal definition of unsupervised learning and take a quick look at some other types... | [0.002350258408114314, 0.011659093201160431, 0.03043528087437153, -0.01986316218972206, 0.0013765105977654457, 0.007460008375346661, -0.005968653596937656, -0.0033773845061659813, -0.0333079993724823, -0.04202967509627342, 0.006421559490263462, 0.02164890617132187, -0.02615208551287651, -0.0014177574776113033, 0.005282... |
1.8 Machine Learning Overview | Jupyter Notebooks --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=SRDUSIcnW8M | So far in the videos, you've seen supervised learning and unsupervised learning, and also examples of both. For you to more deeply understand these concepts, I'd like to invite you in this class to see, run, and maybe later write code yourself to implement these concepts. The most widely used tool by machine learning a... | 500 | 1.8 Machine Learning Overview | Jupyter Notebooks --[Machine Learning | Andrew Ng]: So far in the videos, you've seen supervised learning and unsupervised learning, and also examples of both. For you to more deeply understand these concepts, I'd like to invite you in this class to see, run, and maybe later write code y... | [-0.008482740260660648, 0.00021397847740445286, 0.03213098272681236, -0.024595245718955994, -0.011155844666063786, -0.006370450835675001, -0.002815826563164592, -0.006642462685704231, -0.042796533554792404, -0.029632503166794777, 0.01602519303560257, 0.013009555637836456, -0.031969789415597916, 0.024635544046759605, 0.... |
1.8 Machine Learning Overview | Jupyter Notebooks --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=SRDUSIcnW8M | or cell, which looks like this, which is a code cell. So here we've already provided the code. And if you want to run this code cell, hitting shift enter will run the code in this code cell. Oh, and by the way, if you click on a markdown cell, so it's showing all this formatting, go ahead and hit shift enter on your ke... | 275 | 1.8 Machine Learning Overview | Jupyter Notebooks --[Machine Learning | Andrew Ng]: or cell, which looks like this, which is a code cell. So here we've already provided the code. And if you want to run this code cell, hitting shift enter will run the code in this code cell. Oh, and by the way, if you click on a markdow... | [-0.008251857943832874, -0.012371154502034187, 0.020443912595510483, -0.019064180552959442, -0.0010745988693088293, -0.01479231845587492, 0.006135826930403709, -0.009638223797082901, -0.032158367335796356, -0.018652914091944695, 0.03162769973278046, 0.027063973248004913, -0.02783343754708767, 0.03303396701812744, 0.007... |
1.9 Machine Learning Overview | Linear regression model part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=isx7QB_j4jY | In this video, we'll look at what the overall process of supervised learning is like. Specifically, you see the first model of this course, a linear regression model. That just means filling a straight line to your data. It's probably the most widely used learning algorithm in the world today. And as you get familiar w... | 500 | 1.9 Machine Learning Overview | Linear regression model part 1 --[Machine Learning | Andrew Ng]: In this video, we'll look at what the overall process of supervised learning is like. Specifically, you see the first model of this course, a linear regression model. That just means filling a straight line to your data. It... | [0.011048629879951477, 0.013754809275269508, 0.00791381299495697, -0.02319764532148838, -0.016377819702029228, -0.013652447611093521, 0.009730727411806583, 0.020792152732610703, -0.024438776075839996, -0.03326744586229324, -0.016851240769028664, 0.030017470940947533, -0.024681884795427322, -0.004468713421374559, 0.0030... |
1.9 Machine Learning Overview | Linear regression model part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=isx7QB_j4jY | is addressing what's called a regression problem. So linear regression is one example of a regression model, but there are other models for addressing regression problems too. And we'll see some of those later in course two of this specialization. And just to remind you, in contrast with the regression model, the other... | 500 | 1.9 Machine Learning Overview | Linear regression model part 1 --[Machine Learning | Andrew Ng]: is addressing what's called a regression problem. So linear regression is one example of a regression model, but there are other models for addressing regression problems too. And we'll see some of those later in course two... | [0.00032095922506414354, 0.007179827895015478, 0.015415702015161514, -0.009987365454435349, -0.008351781405508518, -0.008107088506221771, 0.006188174244016409, 0.007276417221873999, -0.025280721485614777, -0.034926801919937134, -0.017927031964063644, 0.03956310451030731, -0.028255680575966835, 0.005888746585696936, 0.0... |
1.9 Machine Learning Overview | Linear regression model part 1 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=isx7QB_j4jY | price of your client's house, you first train your model to learn from the training set and that model can then predict your client's house's price. In machine learning, the standard notation to denote the input here is lowercase x and we call this the input variable. It's also called a feature or an input feature. For... | 460 | 1.9 Machine Learning Overview | Linear regression model part 1 --[Machine Learning | Andrew Ng]: price of your client's house, you first train your model to learn from the training set and that model can then predict your client's house's price. In machine learning, the standard notation to denote the input here is low... | [-0.0014075615908950567, -0.002409252105280757, 0.01680631935596466, -0.01974157989025116, -0.008533038198947906, -0.007351140957325697, 0.006042611785233021, -0.006156255956739187, -0.021001404151320457, -0.03792461380362511, 0.005506861489266157, 0.03618423640727997, -0.01909218542277813, -0.002500167116522789, 0.000... |
1.10 Machine Learning Overview | Linear regression model part 2 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=vrTHO5zRq6s | Let's look in this video at the process of how supervised learning works. Supervised learning algorithm will input a data set and then what exactly does it do and what does it output? Let's find out in this video. Recall that a training set in supervised learning includes both the input features, such as the size of th... | 500 | 1.10 Machine Learning Overview | Linear regression model part 2 --[Machine Learning | Andrew Ng]: Let's look in this video at the process of how supervised learning works. Supervised learning algorithm will input a data set and then what exactly does it do and what does it output? Let's find out in this video. Recall t... | [-0.0011746789095923305, -0.005352677311748266, 0.007258976344019175, -0.00549654895439744, -0.01449179369956255, -0.005990290082991123, -0.0009441573638468981, 0.018664071336388588, -0.024628201499581337, -0.035340096801519394, 0.009665555320680141, 0.02979450114071369, -0.025726858526468277, 0.01389014907181263, -0.0... |
1.10 Machine Learning Overview | Linear regression model part 2 --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=vrTHO5zRq6s | a simple notation, but means exactly the same thing as f w b of x. Let's plot the training set on the graph where the input feature x is on the horizontal axis, and the output target y is on the vertical axis. Remember, the algorithm learns from this data and generates a best fit line like maybe this one here. This str... | 500 | 1.10 Machine Learning Overview | Linear regression model part 2 --[Machine Learning | Andrew Ng]: a simple notation, but means exactly the same thing as f w b of x. Let's plot the training set on the graph where the input feature x is on the horizontal axis, and the output target y is on the vertical axis. Remember, th... | [-0.0034556426107883453, -0.0021222862415015697, 0.008580425754189491, 0.00687216455116868, -0.01615675911307335, -0.017421655356884003, 0.004925920628011227, 0.01115585770457983, -0.020094888284802437, -0.025936879217624664, -0.01282499823719263, 0.029314281418919563, -0.008045779541134834, 0.010588610544800758, -0.01... |
1.11 Machine Learning Overview | Cost function formula --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=ZzeDtSmrRoU | In order to implement linear regression, the first key step is for us to define something called a cost function. This is something we'll build in this video. And the cost function will tell us how well the model is doing so that we can try to get it to do better. Let's look at what this means. Recall that you have a t... | 500 | 1.11 Machine Learning Overview | Cost function formula --[Machine Learning | Andrew Ng]: In order to implement linear regression, the first key step is for us to define something called a cost function. This is something we'll build in this video. And the cost function will tell us how well the model is doing so that w... | [0.002762416610494256, -0.0004078101774211973, 0.01721123792231083, 0.009769757278263569, -0.016863925382494926, -0.012670455500483513, 0.01181503850966692, 0.011326229199767113, -0.010239271447062492, -0.03154107183218002, -0.0029392882715910673, 0.04471319913864136, -0.011088255792856216, 0.0027431214693933725, -0.01... |
1.11 Machine Learning Overview | Cost function formula --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=ZzeDtSmrRoU | is 0.5. Recall that you have a training set, like the one shown here, with linear regression, what you want to do is to choose values for the parameters w and b so that the straight line you get from the function f somehow fits the data well, like maybe this line shown here. And when I say that the line fits the data, ... | 500 | 1.11 Machine Learning Overview | Cost function formula --[Machine Learning | Andrew Ng]: is 0.5. Recall that you have a training set, like the one shown here, with linear regression, what you want to do is to choose values for the parameters w and b so that the straight line you get from the function f somehow fits the... | [-0.0016195555217564106, 0.0009552072151564062, 0.02180119976401329, -0.003153175348415971, -0.01984451152384281, -0.02534439042210579, 0.014463622123003006, 0.006583988666534424, -0.02431316301226616, -0.02172187529504299, -0.014926351606845856, 0.051878657191991806, -0.013399343006312847, 0.001801342354156077, -0.016... |
1.11 Machine Learning Overview | Cost function formula --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=ZzeDtSmrRoU | learning people use actually divides by two times m. The extra division by two is just meant to make some of our later calculations a little bit neater. But the cost function still works whether you include this division by two or not. So this expression right here is the cost function. And we're going to write J of WB... | 304 | 1.11 Machine Learning Overview | Cost function formula --[Machine Learning | Andrew Ng]: learning people use actually divides by two times m. The extra division by two is just meant to make some of our later calculations a little bit neater. But the cost function still works whether you include this division by two or ... | [0.015567927621304989, 0.002886767964810133, 0.03157402575016022, 0.003418371547013521, -0.008789177052676678, -0.027991339564323425, 0.013145104050636292, 0.007751745171844959, -0.011527740396559238, -0.03951263800263405, -0.018995964899659157, 0.05080196261405945, -0.015103982761502266, 0.006817411631345749, -0.01748... |
1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=33VvmIZof0E | We've seen the mathematical definition of the cost function. Now let's build some intuition about what the cost function is really doing. In this video, we'll walk through one example to see how the cost function can be used to find the best parameters for your model. I know this video is a little bit longer than the o... | 500 | 1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng]: We've seen the mathematical definition of the cost function. Now let's build some intuition about what the cost function is really doing. In this video, we'll walk through one example to see how the cost function can be used to f... | [-0.007065854966640472, -6.93207675794838e-06, 0.022957511246204376, 0.015226787887513638, -0.012599905952811241, -0.02055877074599266, 0.01077477727085352, 0.010318495333194733, -0.02422206476330757, -0.029514936730265617, -0.011798152700066566, 0.044924236834049225, -0.002731174696236849, -0.0017974257934838533, -0.0... |
1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=33VvmIZof0E | and you'll be able to see how the two are related. First notice that for f subscript w, when the parameter w is fixed, that is, is always a constant value, then f w is only a function of x, which means that the estimated value of y depends on the value of the input x. In contrast, looking to the right, the cost functio... | 500 | 1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng]: and you'll be able to see how the two are related. First notice that for f subscript w, when the parameter w is fixed, that is, is always a constant value, then f w is only a function of x, which means that the estimated value of... | [-0.0016798374708741903, -0.012061362154781818, 0.020029326900839806, 0.011488543823361397, -0.018304435536265373, -0.02255230024456978, 0.0197718795388937, 0.009158654138445854, -0.033339302986860275, -0.02903994917869568, -0.0006307435687631369, 0.04482141137123108, -0.004115923773497343, 0.01031072624027729, -0.0147... |
1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=33VvmIZof0E | that because the cost function is a function of the parameter w, the horizontal axis is now labeled w and not x. And the vertical axis is now j and not y. So you have j of 1 equals to zero. In other words, when w equals 1, j of w is zero. So let me go ahead and plot that. Now let's look at how f and j change for differ... | 500 | 1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng]: that because the cost function is a function of the parameter w, the horizontal axis is now labeled w and not x. And the vertical axis is now j and not y. So you have j of 1 equals to zero. In other words, when w equals 1, j of w... | [-0.0013879804173484445, -0.01074820477515459, 0.01671503856778145, 0.013013757765293121, -0.012947898358106613, -0.017426317557692528, 0.014897327870130539, -0.00043384681339375675, -0.032007522881031036, -0.03474725782871246, -0.0059075611643493176, 0.05121203511953354, -0.012414439581334591, 0.005986591801047325, -0... |
1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=33VvmIZof0E | do the graphs for f and j look like when w is equal to 0? It turns out that if w is equal to 0, then f of x is just this horizontal line that is exactly on the x-axis, and so the error for each example is a line that goes from each point down to the horizontal line that represents f of x equals 0. So the cost j when w ... | 500 | 1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng]: do the graphs for f and j look like when w is equal to 0? It turns out that if w is equal to 0, then f of x is just this horizontal line that is exactly on the x-axis, and so the error for each example is a line that goes from ea... | [-0.005594865884631872, -0.004575836006551981, 0.017336571589112282, 0.0074859499000012875, -0.023424621671438217, -0.016748670488595963, 0.011614327318966389, 0.0014321941416710615, -0.023986395448446274, -0.034437984228134155, -0.009177801199257374, 0.05215342342853546, -0.016683347523212433, 0.0068392581306397915, -... |
1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=33VvmIZof0E | w that results in the smallest possible value of j of w, you'd end up picking w equals 1. And as you can see, that's actually a pretty good choice. This results in a line that fits the training data very well. So that's how in linear regression, you use the cost function to find the value of w that minimizes j. Or in t... | 220 | 1.12 Machine Learning Overview | Cost function intuition --[Machine Learning | Andrew Ng]: w that results in the smallest possible value of j of w, you'd end up picking w equals 1. And as you can see, that's actually a pretty good choice. This results in a line that fits the training data very well. So that's how in li... | [0.0007702865987084806, -0.00468400726094842, 0.02870200015604496, 0.01641063392162323, -0.019865505397319794, -0.00999919231981039, 0.010670234449207783, 0.0060128034092485905, -0.027638964354991913, -0.03510680049657822, -0.011454224586486816, 0.05386940389871597, 0.0012266450794413686, 0.002067939378321171, -0.00329... |
1.13 Machine Learning Overview | Visualizing the cost function --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=75eDNh4A07Q | In the last video, you saw one visualization of the cost function J of w or J of wb. Let's look at some further, richer visualizations so you can get an even better intuition about what the cost function is doing. Here is what we've seen so far. There's the model, the model's parameters w and b, the cost function J of ... | 500 | 1.13 Machine Learning Overview | Visualizing the cost function --[Machine Learning | Andrew Ng]: In the last video, you saw one visualization of the cost function J of w or J of wb. Let's look at some further, richer visualizations so you can get an even better intuition about what the cost function is doing. Here is w... | [-0.010770678520202637, -0.013417710550129414, 0.03476347401738167, 0.022506285458803177, -0.008234484121203423, -0.02301482856273651, 0.013795857317745686, 0.007393432315438986, -0.031451426446437836, -0.03765825927257538, -0.010790238156914711, 0.037814732640981674, -0.004717061761766672, 0.0017880500527098775, -0.00... |
1.13 Machine Learning Overview | Visualizing the cost function --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=75eDNh4A07Q | lot like the u-shaped curve you saw in the last video, except instead of having one parameter, w, as input in the j, you now have two parameters, w and b, as inputs into this soup bowl or this hammock-shaped function j. And I just want to point out that any single point on the surface represents a particular choice of ... | 500 | 1.13 Machine Learning Overview | Visualizing the cost function --[Machine Learning | Andrew Ng]: lot like the u-shaped curve you saw in the last video, except instead of having one parameter, w, as input in the j, you now have two parameters, w and b, as inputs into this soup bowl or this hammock-shaped function j. And... | [-0.014728846959769726, -0.016474096104502678, 0.03469344228506088, 0.021273532882332802, -0.0026872872840613127, -0.0017353332368656993, -0.0021286753471940756, -0.0059001329354941845, -0.031282272189855576, -0.026509279385209084, 0.003338450565934181, 0.032366443425416946, -0.01180687639862299, 0.007827178575098515, ... |
1.13 Machine Learning Overview | Visualizing the cost function --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=75eDNh4A07Q | the figure on the upper left, you see also that these three points correspond to different functions f, all three of which are actually pretty bad for predicting housing prices in this case. Now, the bottom of the bow, where the cost function j is at a minimum, is this point right here, at the center of this concentric... | 268 | 1.13 Machine Learning Overview | Visualizing the cost function --[Machine Learning | Andrew Ng]: the figure on the upper left, you see also that these three points correspond to different functions f, all three of which are actually pretty bad for predicting housing prices in this case. Now, the bottom of the bow, wher... | [-0.019732855260372162, -0.009906157851219177, 0.03543967753648758, 0.008336800150573254, -0.004310768097639084, 0.00016192281327676028, -0.010237246751785278, -0.009442634880542755, -0.041717108339071274, -0.020845310762524605, -0.0016032945131883025, 0.03710836172103882, -0.016594139859080315, 0.0061516184359788895, ... |
1.14 Machine Learning Overview | Visualizing examples --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=GY0KyF3h8hA | Let's look at some more visualizations of w and b. Here's one example. Over here, you have a particular point on the graph J. For this point, w equals about negative 0.15 and b equals about 800. So this point corresponds to one pair of values for w and b that use a particular cost J. And in fact, this particular pair o... | 500 | 1.14 Machine Learning Overview | Visualizing examples --[Machine Learning | Andrew Ng]: Let's look at some more visualizations of w and b. Here's one example. Over here, you have a particular point on the graph J. For this point, w equals about negative 0.15 and b equals about 800. So this point corresponds to one pair... | [-0.024980470538139343, -0.011996160261332989, 0.01890663243830204, 0.011199050582945347, -0.02210824377834797, -0.02148900181055069, 0.017457343637943268, -0.00026206602342426777, -0.023004168644547462, -0.035494402050971985, -0.00976293720304966, 0.037523407489061356, -0.012365070171654224, 0.004950639326125383, -0.0... |
1.14 Machine Learning Overview | Visualizing examples --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=GY0KyF3h8hA | close to the minimum possible sum of squared errors among all possible straight line fits. I hope that by looking at these figures, you can get a better sense of how different choices of the parameters affect the line f of x and how this corresponds to different values for the cost j. And hopefully, you can see how the... | 423 | 1.14 Machine Learning Overview | Visualizing examples --[Machine Learning | Andrew Ng]: close to the minimum possible sum of squared errors among all possible straight line fits. I hope that by looking at these figures, you can get a better sense of how different choices of the parameters affect the line f of x and how... | [-0.021490328013896942, 0.0025370526127517223, 0.028653770685195923, -0.006371975876390934, -0.01388420071452856, -0.008907351642847061, 0.010429918766021729, 0.006982344202697277, -0.03648794814944267, -0.03190012276172638, -0.010235405527055264, 0.03672941029071808, -0.012549439445137978, 0.0066670989617705345, -0.00... |
1.15 Machine Learning Overview | Gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=6W3tzcOnWfQ | Welcome back. In the last video we saw visualizations of the cost function j and how you can try different choices of the parameters w and b and see what cost value that gets you. It would be nice if we had a more systematic way to find the values of w and b that result in the smallest possible cost j of w, b. It turns... | 500 | 1.15 Machine Learning Overview | Gradient descent --[Machine Learning | Andrew Ng]: Welcome back. In the last video we saw visualizations of the cost function j and how you can try different choices of the parameters w and b and see what cost value that gets you. It would be nice if we had a more systematic way to find... | [-0.017566446214914322, 0.009759869426488876, 0.03851156309247017, 0.005384755786508322, -0.011620777659118176, -0.006150235887616873, 0.018622281029820442, 0.0008553909137845039, -0.030619200319051743, -0.03700699657201767, -0.0044510019943118095, 0.03758770599961281, -0.012676612474024296, 0.01616746559739113, -2.879... |
1.15 Machine Learning Overview | Gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=6W3tzcOnWfQ | regression with the squared error cost function, you always end up with a bow shape or a hammer shape. But this is a type of cost function you might get if you're training a neural network model. Notice the axes. That is w and b on the bottom axis. For different values of w and b, you get different points on the surfac... | 500 | 1.15 Machine Learning Overview | Gradient descent --[Machine Learning | Andrew Ng]: regression with the squared error cost function, you always end up with a bow shape or a hammer shape. But this is a type of cost function you might get if you're training a neural network model. Notice the axes. That is w and b on the ... | [-0.010462560690939426, 0.005613489542156458, 0.02383655682206154, -0.004882306791841984, -0.005939198192209005, -0.0003838709380943328, 0.015168718062341213, 0.003258748445659876, -0.026495404541492462, -0.03182639181613922, -0.009658259339630604, 0.02716011554002762, -0.01863851211965084, 0.019289929419755936, -0.008... |
1.15 Machine Learning Overview | Gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=6W3tzcOnWfQ | at this point over here, right? Now, imagine if you try gradient descent again, but this time you choose a different starting point by choosing parameters that place your starting point just a couple steps to the right over here. If you then repeat the gradient descent process, which means you look around, take a littl... | 244 | 1.15 Machine Learning Overview | Gradient descent --[Machine Learning | Andrew Ng]: at this point over here, right? Now, imagine if you try gradient descent again, but this time you choose a different starting point by choosing parameters that place your starting point just a couple steps to the right over here. If you... | [-0.01683114655315876, 0.009932599030435085, 0.03559780865907669, -0.03750716894865036, 0.00042829837184399366, 0.015575675293803215, -0.006179266609251499, -0.015627987682819366, -0.030340526252985, -0.028771189972758293, 0.015091796405613422, 0.03253759816288948, -0.011547708883881569, 0.011397314257919788, -0.002837... |
1.16 Machine Learning Overview | Implementing gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Dz4JZzgn-hg | Let's take a look at how you can actually implement the gradient descent algorithm. Let me write down the gradient descent algorithm. Here it is. On each step, w, the parameter, is updated to the old value of w minus alpha times this term d over dw of the cost function j of wb. So what this expression is saying is upda... | 500 | 1.16 Machine Learning Overview | Implementing gradient descent --[Machine Learning | Andrew Ng]: Let's take a look at how you can actually implement the gradient descent algorithm. Let me write down the gradient descent algorithm. Here it is. On each step, w, the parameter, is updated to the old value of w minus alpha ... | [0.005584876984357834, -0.006587459240108728, 0.02301008626818657, -0.02548202872276306, -0.017553409561514854, -0.0008924626745283604, 0.00912514328956604, 0.016790788620710373, -0.027217645198106766, -0.04615166038274765, 0.01040713395923376, 0.03650057315826416, -0.021879306063055992, 0.017527112737298012, -0.002729... |
1.16 Machine Learning Overview | Implementing gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Dz4JZzgn-hg | about the details of this derivative right now, but later on you get to see more about the derivative term. But for now, you can think of this derivative term that I drew a magenta box around as telling you in which direction you want to take your baby step. And in combination with the learning rate alpha, it also dete... | 500 | 1.16 Machine Learning Overview | Implementing gradient descent --[Machine Learning | Andrew Ng]: about the details of this derivative right now, but later on you get to see more about the derivative term. But for now, you can think of this derivative term that I drew a magenta box around as telling you in which directi... | [-0.015832915902137756, -0.003789101494476199, 0.026618031784892082, -0.02158324234187603, -0.012313767336308956, 0.0014050573809072375, 0.005298237316310406, 0.012573963031172752, -0.03369535878300667, -0.05589006096124649, 0.011045312508940697, 0.03632333502173424, -0.03603712096810341, 0.026071621105074883, 0.003619... |
1.16 Machine Learning Overview | Implementing gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Dz4JZzgn-hg | okay? In contrast, here's an incorrect implementation of gradient descent that does not do a simultaneous update. In this incorrect implementation, we compute tempW same as before, so far that's okay. And now here's where things start to differ. We then update W with the value in tempW before calculating the new value ... | 441 | 1.16 Machine Learning Overview | Implementing gradient descent --[Machine Learning | Andrew Ng]: okay? In contrast, here's an incorrect implementation of gradient descent that does not do a simultaneous update. In this incorrect implementation, we compute tempW same as before, so far that's okay. And now here's where t... | [-0.018350405618548393, 0.0030320168007165194, 0.03693342208862305, -0.030446166172623634, -0.009769652970135212, -0.004888057243078947, 0.009750268422067165, 0.0020692667458206415, -0.030937233939766884, -0.03615805506706238, 0.01891900971531868, 0.039905671030282974, -0.02982587181031704, 0.022123869508504868, 0.0066... |
1.17 Machine Learning Overview | Gradient descent intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=rGvoO8U2Ozc | Now, let's dive more deeply into gradient descent to gain better intuition about what it's doing and why it might make sense. Here's the gradient descent algorithm that you saw in the previous video. And as a reminder, this variable, this Greek symbol alpha is the learning rate. And the learning rate controls how big o... | 500 | 1.17 Machine Learning Overview | Gradient descent intuition --[Machine Learning | Andrew Ng]: Now, let's dive more deeply into gradient descent to gain better intuition about what it's doing and why it might make sense. Here's the gradient descent algorithm that you saw in the previous video. And as a reminder, this v... | [-0.008080286905169487, 0.0032749646343290806, 0.025426484644412994, -0.02381558157503605, -0.01667606271803379, -0.0036019778344780207, 0.011637158691883087, 0.007719444576650858, -0.03770800307393074, -0.05500265210866928, 0.01197222713381052, 0.036986321210861206, -0.01760394126176834, 0.024833671748638153, 0.008183... |
1.17 Machine Learning Overview | Gradient descent intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=rGvoO8U2Ozc | over 1, for instance. And when the tangent line is pointing up into the right, the slope is positive, which means that this derivative is a positive number, so it's greater than zero. And so the updated w is going to be w minus the learning rate times some positive number. The learning rate is always a positive number.... | 500 | 1.17 Machine Learning Overview | Gradient descent intuition --[Machine Learning | Andrew Ng]: over 1, for instance. And when the tangent line is pointing up into the right, the slope is positive, which means that this derivative is a positive number, so it's greater than zero. And so the updated w is going to be w min... | [-0.009986313059926033, 0.005173355806618929, 0.02967062033712864, -0.020876899361610413, -0.012515654787421227, 0.003535181051120162, 0.005019367206841707, 0.000855127174872905, -0.035174887627363205, -0.05100620910525322, 0.010707110166549683, 0.04474182799458504, -0.02967062033712864, 0.028700821101665497, -0.007679... |
1.17 Machine Learning Overview | Gradient descent intuition --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=rGvoO8U2Ozc | it's too small? What happens if it's too big? In the next video, let's take a deeper look at the parameter alpha to help build intuitions about what it does, as well as how to make a good choice for a good value of alpha for your implementation of gradient descent. | 51 | 1.17 Machine Learning Overview | Gradient descent intuition --[Machine Learning | Andrew Ng]: it's too small? What happens if it's too big? In the next video, let's take a deeper look at the parameter alpha to help build intuitions about what it does, as well as how to make a good choice for a good value of alpha for ... | [0.009353122673928738, -0.005120371002703905, 0.044725414365530014, -0.03391500562429428, 0.00019602972315624356, 0.005100498907268047, 0.001881222939118743, 0.012777742929756641, -0.02670806646347046, -0.02613840065896511, 0.0031397875864058733, 0.03669709712266922, -0.019236167892813683, 0.006113974843174219, 0.01599... |
1.18 Machine Learning Overview | Learning rate --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Eu8lt4j9xiU | The choice of the learning rate alpha will have a huge impact on the efficiency of your implementation of gradient descent, and if alpha, the learning rate, is chosen poorly, gradient descent may not even work at all. In this video, let's take a deeper look at the learning rate. This will also help you choose better le... | 500 | 1.18 Machine Learning Overview | Learning rate --[Machine Learning | Andrew Ng]: The choice of the learning rate alpha will have a huge impact on the efficiency of your implementation of gradient descent, and if alpha, the learning rate, is chosen poorly, gradient descent may not even work at all. In this video, let's... | [-0.0061140465550124645, -0.011307284235954285, 0.03152645751833916, -0.028693199157714844, -0.0051610409282147884, 0.0032131746411323547, 0.003544794861227274, 0.003831340465694666, -0.028744712471961975, -0.037527818232774734, 0.02302667871117592, 0.03817174211144447, -0.021867617964744568, 0.021082032471895218, -0.0... |
1.18 Machine Learning Overview | Learning rate --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Eu8lt4j9xiU | the learning rate is too big, then you may take a huge step going from here all the way out here. So, now you've gotten to this point here, and again, if the learning rate is too big, then you take another huge step at the next iteration and way overshoot the minimum again. So, now you're at this point on the right, an... | 500 | 1.18 Machine Learning Overview | Learning rate --[Machine Learning | Andrew Ng]: the learning rate is too big, then you may take a huge step going from here all the way out here. So, now you've gotten to this point here, and again, if the learning rate is too big, then you take another huge step at the next iteration ... | [-0.010012581944465637, -0.009986215271055698, 0.03485617786645889, -0.011021090671420097, 6.910839874763042e-05, -0.0023960324469953775, 0.008272409439086914, -0.00337817519903183, -0.028923770412802696, -0.03332693502306938, 0.01628115586936474, 0.03311600536108017, -0.015305605717003345, 0.011152922175824642, 0.0054... |
1.18 Machine Learning Overview | Learning rate --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Eu8lt4j9xiU | local minimum even with a fixed learning rate alpha. Here's what I mean. To illustrate this, let's look at another example. Here's the cost function J of W that we want to minimize. Let's initialize gradient descent up here at this point. If we take one update step, maybe it'll take us to that point. And because this d... | 322 | 1.18 Machine Learning Overview | Learning rate --[Machine Learning | Andrew Ng]: local minimum even with a fixed learning rate alpha. Here's what I mean. To illustrate this, let's look at another example. Here's the cost function J of W that we want to minimize. Let's initialize gradient descent up here at this point.... | [-0.0067804064601659775, 0.0007879509939812124, 0.032303933054208755, -0.015086568892002106, -0.002055166754871607, 0.000535987492185086, 0.00717499852180481, 0.010995964519679546, -0.036907508969306946, -0.035618506371974945, 0.00804310105741024, 0.045983124524354935, -0.008970391936600208, 0.015954671427607536, -0.01... |
1.19 Machine Learning Overview| Gradient descent for Linear Regression--[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=ZSg_NglG3aA | So, previously you took a look at the linear regression model and then the cost function and then the gradient descent algorithm. In this video, we're going to put it all together and use the square error cost function for the linear regression model with gradient descent. This will allow us to train the linear regress... | 500 | 1.19 Machine Learning Overview| Gradient descent for Linear Regression--[Machine Learning|Andrew Ng]: So, previously you took a look at the linear regression model and then the cost function and then the gradient descent algorithm. In this video, we're going to put it all together and use the square error cost function... | [0.004364957567304373, 0.011336445808410645, 0.029680399224162102, -0.006074265576899052, -0.01045232079923153, -0.017865872010588646, 0.019319765269756317, -0.0026736592408269644, -0.022947952151298523, -0.039530206471681595, 0.004453369881957769, 0.04833216220140457, -0.0258819367736578, 0.02531871758401394, -0.00585... |
1.19 Machine Learning Overview| Gradient descent for Linear Regression--[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=ZSg_NglG3aA | two that appears from computing the derivative. For the other derivative with respect to b, this is quite similar. I can write it out like this and once again, plug in the definition of f of x i giving this equation. And by the rules of calculus, this is equal to this, where there's no x i anymore at the end. And so th... | 448 | 1.19 Machine Learning Overview| Gradient descent for Linear Regression--[Machine Learning|Andrew Ng]: two that appears from computing the derivative. For the other derivative with respect to b, this is quite similar. I can write it out like this and once again, plug in the definition of f of x i giving this equation. A... | [-0.008431332185864449, 0.007493788842111826, 0.019799862056970596, -0.015223602764308453, -0.009126294404268265, -0.0023799173068255186, 0.009132849983870983, 0.002799517009407282, -0.028768805786967278, -0.028716357424855232, -0.0003222804516553879, 0.04447757080197334, -0.010332643054425716, 0.011814354918897152, -0... |
1.20 Machine Learning Overview | Running gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=3xyYI4wPuTs | Let's see what happens when you run gradient descent for linear regression. Let's go see the algorithm in action. Here's a plot of the model and data on the upper left, and a contour plot of the cost function on the upper right. And at the bottom is the surface plot of the same cost function. Often W and B will both be... | 500 | 1.20 Machine Learning Overview | Running gradient descent --[Machine Learning | Andrew Ng]: Let's see what happens when you run gradient descent for linear regression. Let's go see the algorithm in action. Here's a plot of the model and data on the upper left, and a contour plot of the cost function on the upper right... | [-0.015172421000897884, -0.008556770160794258, 0.030159974470734596, -0.0037633944302797318, -0.003492694115266204, -0.004707544110715389, 0.013218097388744354, 0.0026624363381415606, -0.03610217571258545, -0.03882238268852234, 0.004740556236356497, 0.04093516618013382, -0.015159216709434986, 0.010009308345615864, -0.0... |
1.20 Machine Learning Overview | Running gradient descent --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=3xyYI4wPuTs | your first machine learning model. I hope you go and celebrate or, I don't know, maybe take a nap in your hammock. In the optional lab that follows this video, you see a review of the gradient descent algorithm as well as how to implement it in code. You also see a plot that shows how the cost decreases as you continue... | 376 | 1.20 Machine Learning Overview | Running gradient descent --[Machine Learning | Andrew Ng]: your first machine learning model. I hope you go and celebrate or, I don't know, maybe take a nap in your hammock. In the optional lab that follows this video, you see a review of the gradient descent algorithm as well as how t... | [-0.015758763998746872, 0.008127921260893345, 0.02866939641535282, -0.030926406383514404, 0.0017498541856184602, -0.003029498038813472, 0.007402454037219286, 0.0034594046883285046, -0.04156659543514252, -0.02556600794196129, -0.005477950442582369, 0.02621086686849594, -0.014952688477933407, 0.005891063716262579, 0.0057... |
2.1 Linear Regression with Multiple Variables | Multiple features --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=fX86EFWljY0 | Welcome back. In this week, we'll learn to make linear regression much faster and much more powerful, and by the end of this week, you'll be two-thirds of the way to finishing this first course. Let's start by looking at a version of linear regression that can look at not just one feature, but a lot of different featur... | 500 | 2.1 Linear Regression with Multiple Variables | Multiple features --[Machine Learning | Andrew Ng]: Welcome back. In this week, we'll learn to make linear regression much faster and much more powerful, and by the end of this week, you'll be two-thirds of the way to finishing this first course. Let's start by looking at... | [-0.00932472012937069, 0.01049031037837267, 0.0013522154185920954, -0.008041261695325375, -0.013934694230556488, -0.017536235973238945, -0.0026225775945931673, -0.014484747312963009, -0.021727122366428375, -0.04114925488829613, 0.00047024679952301085, 0.04526156187057495, -0.013109613209962845, -0.006711964961141348, 0... |
2.1 Linear Regression with Multiple Variables | Multiple features --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=fX86EFWljY0 | notation. You can think of the arrow as an optional signifier that sometimes use just to emphasize that this is a vector and not a number. Now that we have multiple features, let's take a look at what our model would look like. Previously, this is how we defined the model where x was a single feature. So a single numbe... | 500 | 2.1 Linear Regression with Multiple Variables | Multiple features --[Machine Learning | Andrew Ng]: notation. You can think of the arrow as an optional signifier that sometimes use just to emphasize that this is a vector and not a number. Now that we have multiple features, let's take a look at what our model would loo... | [-0.014299682341516018, -0.008783349767327309, -0.00046550133265554905, -0.006281229667365551, -0.014208932407200336, -0.023621054366230965, -0.001124657690525055, -0.004401398357003927, -0.022687619552016258, -0.03785591572523117, -0.0028197469655424356, 0.03873749077320099, -0.03165895491838455, 0.006297435145825148,... |
2.1 Linear Regression with Multiple Variables | Multiple features --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=fX86EFWljY0 | this number b are the parameters of the model. Let me also write x as a list or a vector, again, a row vector that lists all of the features x1, x2, x3 up through xn. This is again a vector. So I'm going to add a little arrow up on top to signify. So in the notation up on top, we can also add little arrows here and her... | 389 | 2.1 Linear Regression with Multiple Variables | Multiple features --[Machine Learning | Andrew Ng]: this number b are the parameters of the model. Let me also write x as a list or a vector, again, a row vector that lists all of the features x1, x2, x3 up through xn. This is again a vector. So I'm going to add a little ... | [-0.026491232216358185, 0.0016162217361852527, 0.0025563447270542383, -0.006313546095043421, -0.016950177028775215, -0.007902625016868114, 0.00010733686212915927, -0.002390198642387986, -0.03521636128425598, -0.04166479781270027, 0.010304339230060577, 0.034031953662633896, -0.026464911177754402, 0.007718384265899658, -... |
2.2 Linear Regression with Multiple Variables | Vectorization part 1 --[Machine Learning |Andrew Ng] | https://www.youtube.com/watch?v=G8yfD_Xu7Ko | In this video, you see a very useful idea called vectorization. When you're implementing a learning algorithm, using vectorization will both make your code shorter and also make it run much more efficiently. Learning how to write vectorized code will allow you to also take advantage of modern numerical linear algebra l... | 500 | 2.2 Linear Regression with Multiple Variables | Vectorization part 1 --[Machine Learning |Andrew Ng]: In this video, you see a very useful idea called vectorization. When you're implementing a learning algorithm, using vectorization will both make your code shorter and also make it run much more efficiently. Learning h... | [-0.01813168078660965, 0.0029333611018955708, -0.00105383712798357, -0.020471684634685516, -0.001308730337768793, -0.007849038578569889, 0.012582531198859215, -0.010830871760845184, -0.034765761345624924, -0.035835474729537964, 0.012863331474363804, 0.0369051918387413, -0.02060539834201336, 0.011840415187180042, 0.0087... |
2.2 Linear Regression with Multiple Variables | Vectorization part 1 --[Machine Learning |Andrew Ng] | https://www.youtube.com/watch?v=G8yfD_Xu7Ko | zero to n means that j goes from zero all the way to n minus one and does not include n itself. And more commonly, this is written range n in Python. But in this video, I added a zero here just to emphasize that it starts from zero. While this implementation is a bit better than the first one, it still doesn't use vect... | 477 | 2.2 Linear Regression with Multiple Variables | Vectorization part 1 --[Machine Learning |Andrew Ng]: zero to n means that j goes from zero all the way to n minus one and does not include n itself. And more commonly, this is written range n in Python. But in this video, I added a zero here just to emphasize that it sta... | [-0.015644527971744537, -0.006628585513681173, 0.008062333799898624, -0.013463897630572319, -0.01604464463889599, -0.005021453835070133, 0.011203243397176266, -0.017765142023563385, -0.042572323232889175, -0.04307913780212402, 0.01565786637365818, 0.039424747228622437, -0.023526810109615326, 0.002919178456068039, 0.015... |
2.3 Linear Regression with Multiple Variables | Vectorization part 2 --[Machine Learning |Andrew Ng] | https://www.youtube.com/watch?v=nRGG50GDNAA | I remember when I first learned about vectorization, I spent many hours on my computer taking an un-vectorized version of an algorithm, running it, see how long it ran, and then running a vectorized version of the code and seeing how much faster that ran. And I just spent hours playing with that. And it frankly blew my... | 500 | 2.3 Linear Regression with Multiple Variables | Vectorization part 2 --[Machine Learning |Andrew Ng]: I remember when I first learned about vectorization, I spent many hours on my computer taking an un-vectorized version of an algorithm, running it, see how long it ran, and then running a vectorized version of the code... | [-0.034406762570142746, -0.00400283420458436, -0.007931165397167206, -0.02068469673395157, -0.0011353215668350458, -0.0041044289246201515, 0.017880680039525032, -0.011033192276954651, -0.05564684420824051, -0.037386875599622726, 0.003210394876077771, 0.027633778750896454, -0.0168782789260149, 0.009055480360984802, 0.00... |
2.3 Linear Regression with Multiple Variables | Vectorization part 2 --[Machine Learning |Andrew Ng] | https://www.youtube.com/watch?v=nRGG50GDNAA | 16. In code, without vectorization, you would be doing something like this, update w1 to be w1 minus the learning rate 0.1 times d1, next update w2 similarly and so on through w16, updated as w16 minus 0.1 times d16. In code without vectorization, you could use a full loop like this for j in range 0, 16, that again goe... | 474 | 2.3 Linear Regression with Multiple Variables | Vectorization part 2 --[Machine Learning |Andrew Ng]: 16. In code, without vectorization, you would be doing something like this, update w1 to be w1 minus the learning rate 0.1 times d1, next update w2 similarly and so on through w16, updated as w16 minus 0.1 times d16. I... | [-0.030325742438435555, -0.0011323033832013607, 0.0038720201700925827, -0.032629307359457016, -0.008042148314416409, -0.009417511522769928, 0.02414677105844021, -0.0019410913810133934, -0.051193322986364365, -0.03948579728603363, 0.0030047958716750145, 0.03542068228125572, -0.019336387515068054, 0.024133220314979553, 0... |
2.4 Linear Regression with Multiple Variables|Gradient descent for multiple linear regression ---ML | https://www.youtube.com/watch?v=odAhNw-e4o0 | So, you've learned about gradient descent, about multiple linear regression, and also vectorization. Let's put it all together to implement gradient descent for multiple linear regression with vectorization. This would be cool. Let's quickly review what multiple linear regression looks like. Using our previous notation... | 500 | 2.4 Linear Regression with Multiple Variables|Gradient descent for multiple linear regression ---ML: So, you've learned about gradient descent, about multiple linear regression, and also vectorization. Let's put it all together to implement gradient descent for multiple linear regression with vectorization. This would ... | [-0.0228939950466156, 0.002952908631414175, 0.013361341319978237, -0.020680127665400505, -0.004219370894134045, 0.003140110755339265, 0.020914535969495773, 0.003506375476717949, -0.030994145199656487, -0.044746167957782745, -0.007097398862242699, 0.04185511916875839, -0.028181230649352074, 0.006960660219192505, -0.0002... |
2.4 Linear Regression with Multiple Variables|Gradient descent for multiple linear regression ---ML | https://www.youtube.com/watch?v=odAhNw-e4o0 | one feature on the left. The error term still takes a prediction f of x minus the target y. One difference is that w and x are now vectors. And just as w on the left has now become w1 here on the right, xi here on the left is now instead xi subscript 1 here on the right. And this is just for j equals 1. For multiple li... | 500 | 2.4 Linear Regression with Multiple Variables|Gradient descent for multiple linear regression ---ML: one feature on the left. The error term still takes a prediction f of x minus the target y. One difference is that w and x are now vectors. And just as w on the left has now become w1 here on the right, xi here on the l... | [-0.009833736345171928, 0.005327428225427866, 0.015489612706005573, -0.030348606407642365, -0.008132374845445156, -0.008638185448944569, 0.018196025863289833, 0.0001348690129816532, -0.027747295796871185, -0.03407977521419525, -0.007186444476246834, 0.03867805004119873, -0.02207171358168125, 0.018498197197914124, -0.00... |
2.4 Linear Regression with Multiple Variables|Gradient descent for multiple linear regression ---ML | https://www.youtube.com/watch?v=odAhNw-e4o0 | feel free also to take a look at the previous optional lab that introduces NumPy and vectorization for a refresher of NumPy functions and how to implement those in code. So that's it. You now know multiple linear regression. This is probably the single most widely used learning algorithm in the world today. But there's... | 120 | 2.4 Linear Regression with Multiple Variables|Gradient descent for multiple linear regression ---ML: feel free also to take a look at the previous optional lab that introduces NumPy and vectorization for a refresher of NumPy functions and how to implement those in code. So that's it. You now know multiple linear regres... | [-0.01162450946867466, 0.003473339369520545, 0.014800896868109703, -0.04649803787469864, 0.0011077317176386714, 0.010309912264347076, 0.01181802898645401, 0.0015940326265990734, -0.040865957736968994, -0.037048954516649246, -0.0028677573427557945, 0.023369135335087776, -0.012572087347507477, 0.0013029193505644798, -0.0... |
2.5 Practical Tips for Linear Regression | Feature scaling part 1-- [Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=AbtWSXHPfS0 | So, welcome back. Let's take a look at some techniques that will make gradient descent work much better. In this video, you see a technique called feature scaling that will enable gradient descent to run much faster. Let's start by taking a look at the relationship between the size of a feature, that is, how big are th... | 500 | 2.5 Practical Tips for Linear Regression | Feature scaling part 1-- [Machine Learning | Andrew Ng]: So, welcome back. Let's take a look at some techniques that will make gradient descent work much better. In this video, you see a technique called feature scaling that will enable gradient descent to run much faster. Let... | [0.0011696895817294717, 0.00904714036732912, 0.04207910969853401, -0.02377350814640522, 0.004540079738944769, 0.002582067158073187, 0.010077325627207756, -0.004044798202812672, -0.03159235045313835, -0.04870927706360817, 0.005946679040789604, 0.03700742870569229, -0.009938647039234638, 0.000327298475895077, 0.004810833... |
2.5 Practical Tips for Linear Regression | Feature scaling part 1-- [Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=AbtWSXHPfS0 | training data, you notice that the horizontal axis is on a much larger scale or much larger range of values compared to the vertical axis. Next, let's look at how the cost function might look in a contour plot. You might see a contour plot where the horizontal axis has a much narrower range, say between 0 and 1, wherea... | 437 | 2.5 Practical Tips for Linear Regression | Feature scaling part 1-- [Machine Learning | Andrew Ng]: training data, you notice that the horizontal axis is on a much larger scale or much larger range of values compared to the vertical axis. Next, let's look at how the cost function might look in a contour plot. You might... | [0.0011806236580014229, -0.013017863966524601, 0.038074903190135956, -0.013916109688580036, -0.003800787031650543, 0.00640838174149394, 0.007051901426166296, -0.0027718262281268835, -0.03279268369078636, -0.043571632355451584, 0.004065568558871746, 0.03777995705604553, -0.004675571341067553, 0.0015551721444353461, -0.0... |
2.6 Practical Tips for Linear Regression | Feature scaling part 2-- [Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=g9bkFTnM-7k | Let's look at how you can implement feature scaling to take features that take on very different ranges of values and scale them to have comparable ranges of value to each other. So how do you actually scale features? Well, if X1 ranges from 3 to 2000, one way to get a scale version of X1 is to take each original X1 va... | 500 | 2.6 Practical Tips for Linear Regression | Feature scaling part 2-- [Machine Learning | Andrew Ng]: Let's look at how you can implement feature scaling to take features that take on very different ranges of values and scale them to have comparable ranges of value to each other. So how do you actually scale features? We... | [0.018670523539185524, -0.011504585854709148, 0.04385553300380707, -0.025836460292339325, -0.0016440940089523792, 0.012559933587908745, -0.003595997579395771, -0.0014690171228721738, -0.023204607889056206, -0.04742547497153282, 0.0060291588306427, 0.020064624026417732, 0.0007284012390300632, -0.005765322130173445, -0.0... |
2.6 Practical Tips for Linear Regression | Feature scaling part 2-- [Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=g9bkFTnM-7k | is often denoted by the lowercase Greek alphabet sigma of each feature. So for instance, maybe feature 1 has a standard deviation of 450 and mean 600, then to z-score normalize x1, take each x1, subtract mu 1, and then divide by the standard deviation, which I'm going to denote as sigma 1. And what you might find is th... | 500 | 2.6 Practical Tips for Linear Regression | Feature scaling part 2-- [Machine Learning | Andrew Ng]: is often denoted by the lowercase Greek alphabet sigma of each feature. So for instance, maybe feature 1 has a standard deviation of 450 and mean 600, then to z-score normalize x1, take each x1, subtract mu 1, and then d... | [0.012259319424629211, -0.012818954885005951, 0.035131968557834625, -0.03181365877389908, -0.0030829356983304024, 0.017526481300592422, -0.005978228524327278, -0.0004092337912879884, -0.024452798068523407, -0.045297592878341675, 0.016012171283364296, 0.023149175569415092, -0.005221073981374502, -0.0014945571310818195, ... |
2.6 Practical Tips for Linear Regression | Feature scaling part 2-- [Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=g9bkFTnM-7k | you check if gradient descent is really working, if it is finding you the global minimum or something close to it? In the next video, let's take a look at how to recognize if gradient descent is converging. And then in the video after that, this will lead to discussion of how to choose a good learning rate for gradient... | 61 | 2.6 Practical Tips for Linear Regression | Feature scaling part 2-- [Machine Learning | Andrew Ng]: you check if gradient descent is really working, if it is finding you the global minimum or something close to it? In the next video, let's take a look at how to recognize if gradient descent is converging. And then in t... | [-0.001770171569660306, 0.0026451805606484413, 0.03248785436153412, -0.04420390725135803, 0.0037082911003381014, 0.00655332999303937, -0.008672833442687988, 0.0020707985386252403, -0.027516596019268036, -0.027368800714612007, 0.01816525310277939, 0.04003879427909851, -0.008672833442687988, 0.006832123268395662, -0.0013... |
2.7 Practical Tips for Linear Regression | Checking gradient descent for convergence -- ML Andrew Ng | https://www.youtube.com/watch?v=9w3HRwTRVUE | When running gradient descent, how can you tell if it is converging? That is, whether it's helping you to find parameters close to the global minimum of the cost function. By learning to recognize what a well-running implementation of gradient descent looks like, we will also, in a later video, be better able to choose... | 500 | 2.7 Practical Tips for Linear Regression | Checking gradient descent for convergence -- ML Andrew Ng: When running gradient descent, how can you tell if it is converging? That is, whether it's helping you to find parameters close to the global minimum of the cost function. By learning to recognize what a well-running i... | [-0.005040606949478388, -0.006871764548122883, 0.027329539880156517, -0.01392730139195919, 0.007449334021657705, -0.005024198442697525, 0.0008126173634082079, -0.001402903231792152, -0.02167198434472084, -0.026003755629062653, 0.016460731625556946, 0.033052727580070496, -0.009385504759848118, 0.005155464168637991, 0.00... |
2.7 Practical Tips for Linear Regression | Checking gradient descent for convergence -- ML Andrew Ng | https://www.youtube.com/watch?v=9w3HRwTRVUE | gradient descent has more or less converged because the curve is no longer decreasing. So looking at this learning curve, you can try to spot whether or not gradient descent is converging. By the way, the number of iterations that gradient descent takes to converge can vary a lot between different applications. In one ... | 319 | 2.7 Practical Tips for Linear Regression | Checking gradient descent for convergence -- ML Andrew Ng: gradient descent has more or less converged because the curve is no longer decreasing. So looking at this learning curve, you can try to spot whether or not gradient descent is converging. By the way, the number of ite... | [-0.010875142179429531, -0.005825264845043421, 0.026468321681022644, -0.02288050949573517, 0.011065702885389328, -0.011131414212286472, 0.002293307799845934, -0.006715646479278803, -0.02352447621524334, -0.02904418483376503, 0.017281947657465935, 0.03214573860168457, 0.003048982471227646, 0.009955190122127533, -0.00808... |
2.8 Practical Tips for Linear Regression | | Choosing the learning rate-[MachineLearning|Andrew Ng] | https://www.youtube.com/watch?v=j4uDxRNjYlA | Your learning algorithm will run much better with an appropriate choice of learning rate. If it's too small, it will run very slowly, and if it's too large, it may not even converge. Let's take a look at how you can choose a good learning rate for your model. Concretely, if you plot the cost for a number of iterations ... | 500 | 2.8 Practical Tips for Linear Regression | | Choosing the learning rate-[MachineLearning|Andrew Ng]: Your learning algorithm will run much better with an appropriate choice of learning rate. If it's too small, it will run very slowly, and if it's too large, it may not even converge. Let's take a look at how you can cho... | [-0.0034659826196730137, -0.0012698069913312793, 0.028020573779940605, -0.01999758929014206, -0.00041599274845793843, 0.0039283353835344315, 0.0018228008411824703, -0.003234806237742305, -0.030202612280845642, -0.028978541493415833, 0.011355916038155556, 0.03310312703251839, -0.009100698865950108, 0.01885334774851799, ... |
2.8 Practical Tips for Linear Regression | | Choosing the learning rate-[MachineLearning|Andrew Ng] | https://www.youtube.com/watch?v=j4uDxRNjYlA | be the most efficient choice for actually training your learning algorithm. One important trade off is that if your learning rate is too small, then gradient descent can take a lot of iterations to converge. So when I am running gradient descent, I will usually try a range of values for the learning rate alpha. So I mi... | 472 | 2.8 Practical Tips for Linear Regression | | Choosing the learning rate-[MachineLearning|Andrew Ng]: be the most efficient choice for actually training your learning algorithm. One important trade off is that if your learning rate is too small, then gradient descent can take a lot of iterations to converge. So when I a... | [0.0010478089097887278, -0.0025901836343109608, 0.02286738157272339, -0.04546652361750603, 0.0005356399342417717, 0.008992714807391167, 0.01282182801514864, 0.00025210282183252275, -0.03141079470515251, -0.043052371591329575, 0.0007581945392303169, 0.031759507954120636, -0.002556653693318367, 0.0027460975106805563, 0.0... |
2.9 Practical Tips for Linear Regression | Feature engineering --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=sO_JcsxQRz8 | The choice of features can have a huge impact on your learning algorithm's performance. In fact, for many practical applications, choosing or entering the right features is a critical step to making the algorithm work well. In this video, let's take a look at how you can choose or engineer the most appropriate features... | 495 | 2.9 Practical Tips for Linear Regression | Feature engineering --[Machine Learning | Andrew Ng]: The choice of features can have a huge impact on your learning algorithm's performance. In fact, for many practical applications, choosing or entering the right features is a critical step to making the algorithm work well.... | [0.00413262564688921, 0.016517236828804016, 0.016503969207406044, -0.0256182998418808, 0.0004817527369596064, -0.013094387948513031, -0.004523998126387596, -0.007562108337879181, -0.02584383636713028, -0.034918367862701416, 0.004391329362988472, 0.04067618399858475, -0.005492478609085083, 0.007356471847742796, -0.00460... |
2.10 Practical Tips for Linear Regression | Polynomial regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=UobsU2oQwBA | So far, we've just been fitting straight lines to our data. Let's take the ideas of multiple linear regression and feature engineering to come up with a new algorithm called polynomial regression, which will let you fit curves, nonlinear functions, to your data. Let's say you have a housing data set that looks like thi... | 500 | 2.10 Practical Tips for Linear Regression | Polynomial regression --[Machine Learning | Andrew Ng]: So far, we've just been fitting straight lines to our data. Let's take the ideas of multiple linear regression and feature engineering to come up with a new algorithm called polynomial regression, which will let you fit ... | [0.008113347925245762, 0.016411390155553818, 0.017915327101945877, -0.012677930295467377, -0.010923336260020733, 0.004294137936085463, 0.01292199082672596, 0.0016070035053417087, -0.010890355333685875, -0.045091744512319565, 0.004205088596791029, 0.028179042041301727, 0.0016507034888491035, -0.00723605090752244, -0.011... |
2.10 Practical Tips for Linear Regression | Polynomial regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=UobsU2oQwBA | well for this dataset as well. So you may ask yourself, how do I decide what features to use? Later, in the second course in this specialization, you see how you can choose different features and different models that include or don't include these features, and you have a process for measuring how well these different... | 440 | 2.10 Practical Tips for Linear Regression | Polynomial regression --[Machine Learning | Andrew Ng]: well for this dataset as well. So you may ask yourself, how do I decide what features to use? Later, in the second course in this specialization, you see how you can choose different features and different models that in... | [0.008227280341088772, 0.004643577616661787, 0.02021711692214012, -0.03494938090443611, -0.01204945519566536, 0.010499387048184872, 0.010545756667852402, -0.0038254866376519203, -0.022191135212779045, -0.02941153384745121, 0.007955687120556831, 0.021661197766661644, -0.008777090348303318, -0.0021528713405132294, -0.007... |
3.1 Classification | Motivations --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=wul3SmrpAeY | Welcome to the third week of this course. By the end of this week, you have completed the first course of this specialization. So let's jump in. Last week, you learned about linear regression, which predicts a number. This week, you learned about classification, where your output variable y can take on only one of a sm... | 500 | 3.1 Classification | Motivations --[Machine Learning | Andrew Ng]: Welcome to the third week of this course. By the end of this week, you have completed the first course of this specialization. So let's jump in. Last week, you learned about linear regression, which predicts a number. This week, you learned about class... | [-0.0014335101004689932, 0.013514573685824871, 0.012053712271153927, -0.02191935107111931, 0.0008872961043380201, 0.00448555126786232, -0.014286834746599197, -0.005688991863280535, -0.04389018565416336, -0.030504323542118073, -0.003151791635900736, 0.04265456646680832, -0.017363009974360466, 0.003874177811667323, -0.00... |
3.1 Classification | Motivations --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=wul3SmrpAeY | because the answer to is it spam is yes or true or one. To be clear, negative and positive do not necessarily mean bad versus good or evil versus good. It's just that negative and positive examples are used to convey the concepts of absence or zero or false versus the presence or true or one of something you might be l... | 500 | 3.1 Classification | Motivations --[Machine Learning | Andrew Ng]: because the answer to is it spam is yes or true or one. To be clear, negative and positive do not necessarily mean bad versus good or evil versus good. It's just that negative and positive examples are used to convey the concepts of absence or zero or ... | [0.004245582967996597, 0.013333937153220177, 0.0052213165909051895, -0.022516926750540733, -0.002964728744700551, -0.0007305764011107385, -0.004346746020019054, -0.0018290923908352852, -0.04156167805194855, -0.03973421826958656, -0.007446902804076672, 0.04469446837902069, -0.007140150293707848, 0.018574833869934082, 0.... |
3.1 Classification | Motivations --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=wul3SmrpAeY | points. This vertical dividing line that we drew just now still makes sense as the cutoff where two meters smaller than this should be classified as zero and two meters greater than this should be classified as one. But once you've added this extra training example on the right, the best fit line for linear regression ... | 441 | 3.1 Classification | Motivations --[Machine Learning | Andrew Ng]: points. This vertical dividing line that we drew just now still makes sense as the cutoff where two meters smaller than this should be classified as zero and two meters greater than this should be classified as one. But once you've added this extra tra... | [0.006017438136041164, 0.009002987295389175, 0.02491709217429161, -0.003872608533129096, -0.01054541114717722, 0.009506095200777054, -0.005471300799399614, 0.0014571930514648557, -0.0415990985929966, -0.030689595267176628, -0.006196173839271069, 0.04652426019310951, -0.004273109138011932, 0.006676112301647663, -0.00470... |
3.2 Classification | Logistic regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Pm8mRCZmYiU | Let's talk about logistic regression, which is probably the single most widely used classification algorithm in the world. This is something that I use all the time in my work. Let's continue with the example of classifying whether a tumor is malignant, where as before we're going to use the label 1 or yes the positive... | 500 | 3.2 Classification | Logistic regression --[Machine Learning | Andrew Ng]: Let's talk about logistic regression, which is probably the single most widely used classification algorithm in the world. This is something that I use all the time in my work. Let's continue with the example of classifying whether a tumor is m... | [0.013036656193435192, 0.010564855299890041, 0.013966002501547337, -0.02174927294254303, 0.003914223983883858, -0.010816552676260471, -0.01104243565350771, 0.0020022885873913765, -0.028448307886719704, -0.036451008170843124, 0.010752014815807343, 0.03534095734357834, -0.005088813602924347, 0.006892648059874773, -0.0181... |
3.2 Classification | Logistic regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Pm8mRCZmYiU | of z is very close to 0. So that's why the sigmoid function has this shape, where it starts very close to 0 and slowly builds up, or grows, to the value of 1. Also, in the sigmoid function, when z is equal to 0, then e to the negative z is e to the negative 0, which is equal to 1, and so g of z is equal to 1 over 1 plu... | 500 | 3.2 Classification | Logistic regression --[Machine Learning | Andrew Ng]: of z is very close to 0. So that's why the sigmoid function has this shape, where it starts very close to 0 and slowly builds up, or grows, to the value of 1. Also, in the sigmoid function, when z is equal to 0, then e to the negative z is e to... | [0.01516462117433548, -0.0007067773258313537, 0.0034042363986372948, -0.0025767500046640635, -0.010254977270960808, -0.006268736906349659, 0.008388662710785866, 0.005777772516012192, -0.02585528790950775, -0.03233211487531662, 0.023059068247675896, 0.035687580704689026, -0.008200080133974552, 0.01011841744184494, -0.02... |
3.2 Classification | Logistic regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=Pm8mRCZmYiU | being 1, what is the chance that it is 0? So y has got to be either 0 or 1, and thus the probability of it being 0 or 1, these two numbers have to add up to 1 or to 100% chance. So that's why if the chance of y being 1 is 0.7 or 70% chance, then the chance of it being 0 has got to be 0.3 or 30% chance. If someday you r... | 429 | 3.2 Classification | Logistic regression --[Machine Learning | Andrew Ng]: being 1, what is the chance that it is 0? So y has got to be either 0 or 1, and thus the probability of it being 0 or 1, these two numbers have to add up to 1 or to 100% chance. So that's why if the chance of y being 1 is 0.7 or 70% chance, the... | [0.023879051208496094, -0.0041962782852351665, 0.00567579735070467, -0.01425248570740223, -0.011467888951301575, -0.010563379153609276, -0.004147822503000498, 0.006725674495100975, -0.032433126121759415, -0.02654089406132698, 0.012572682462632656, 0.04388163238763809, -0.012385319918394089, 0.01381315290927887, -0.0015... |
3.3 Classification | Decision boundary --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=QJdIpRcL_4U | In the last video, you learned about the logistic regression model. Now, let's take a look at the decision boundary to get a better sense of how logistic regression is computing its predictions. To recap, here's how the logistic regression model's outputs are computed in two steps. In the first step, you compute z as w... | 500 | 3.3 Classification | Decision boundary --[Machine Learning | Andrew Ng]: In the last video, you learned about the logistic regression model. Now, let's take a look at the decision boundary to get a better sense of how logistic regression is computing its predictions. To recap, here's how the logistic regression model'... | [0.021033769473433495, -0.011824986897408962, 0.009814023040235043, 0.005408125463873148, -0.004728042520582676, 0.0013788768555969, -0.0011673676781356335, -0.010887838900089264, -0.02798428386449814, -0.04349929094314575, 0.029207782819867134, 0.03964657336473465, -0.019758205860853195, 0.003888513892889023, 0.000352... |
3.3 Classification | Decision boundary --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=QJdIpRcL_4U | equal to 0. So to recap, what you've seen here is that the model predicts 1 whenever w dot x plus b is greater than or equal to 0. And conversely, when w dot x plus b is less than 0, the algorithm predicts y is 0. So given this, let's now visualize how the model makes predictions. I'm going to take an example of a clas... | 500 | 3.3 Classification | Decision boundary --[Machine Learning | Andrew Ng]: equal to 0. So to recap, what you've seen here is that the model predicts 1 whenever w dot x plus b is greater than or equal to 0. And conversely, when w dot x plus b is less than 0, the algorithm predicts y is 0. So given this, let's now visuali... | [0.006120303645730019, -0.007532176095992327, 0.015983710065484047, 0.012621483765542507, -0.012562382966279984, -0.003493563737720251, 0.00977803859859705, -0.0074730743654072285, -0.027764638885855675, -0.03472549840807915, 0.005496452562510967, 0.03598633408546448, -0.021145254373550415, -0.005378249567002058, 0.004... |
3.3 Classification | Decision boundary --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=QJdIpRcL_4U | polynomial features into logistic regression, so f of x, which equals g of z, is now g of this expression over here. And let's say that we end up choosing w1 and w2 to be 1, and b to be negative 1. So z is equal to 1 times x1 squared plus 1 times x2 squared minus 1. And the decision boundary as before will correspond t... | 500 | 3.3 Classification | Decision boundary --[Machine Learning | Andrew Ng]: polynomial features into logistic regression, so f of x, which equals g of z, is now g of this expression over here. And let's say that we end up choosing w1 and w2 to be 1, and b to be negative 1. So z is equal to 1 times x1 squared plus 1 times... | [0.006692839786410332, 0.0073575167916715145, 0.007930060848593712, 0.004968627355992794, -0.021651368588209152, 0.00759443175047636, 0.009147538803517818, 0.005985386203974485, -0.022888589650392532, -0.030667288228869438, 0.0010299207642674446, 0.04683013632893562, -0.005271351430565119, 0.0008172733942046762, -0.006... |
3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=E-ZcnndnY2I | Remember that the cost function gives you a way to measure how well a specific set of parameters fits the training data, and it thereby gives you a way to try to choose better parameters. In this video, we'll look at how the squared error cost function is not an ideal cost function for logistic regression, and we'll ta... | 500 | 3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng]: Remember that the cost function gives you a way to measure how well a specific set of parameters fits the training data, and it thereby gives you a way to try to choose better parameters. In this video, we'll look at how the sq... | [0.008349644020199776, 0.019393132999539375, 0.024002034217119217, -0.012492547743022442, -0.004969569854438305, -0.009428458288311958, 0.020452797412872314, -0.0016980161890387535, -0.023184943944215775, -0.012007400393486023, -0.01567792519927025, 0.049893591552972794, -0.01713336631655693, 0.011005188338458538, -0.0... |
3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=E-ZcnndnY2I | we'll use for logistic regression, I'm going to change a little bit the definition of the cost function j of w and b. In particular, if you look inside the summation, let's call this term inside the loss on a single training example. And I'm going to denote the loss via this capital L, and is a function of the predicti... | 500 | 3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng]: we'll use for logistic regression, I'm going to change a little bit the definition of the cost function j of w and b. In particular, if you look inside the summation, let's call this term inside the loss on a single training ex... | [0.017834331840276718, 0.0019981663208454847, 0.029846278950572014, 0.007533693686127663, 0.000990067608654499, -0.019604651257395744, 0.016601664945483208, 0.01515918131917715, -0.01480511762201786, -0.025072971358895302, 0.0073107643984258175, 0.055233974009752274, -0.024758247658610344, 0.012720074504613876, -0.0194... |
3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=E-ZcnndnY2I | bit higher, but not that high. Whereas in contrast, if the algorithm were to have output 0.1, if it thinks that there's only a 10% chance of the tumor being malignant, but y really is one, it really is malignant, then the loss is this much higher value over here. So when y is equal to one, the loss function incentivize... | 500 | 3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng]: bit higher, but not that high. Whereas in contrast, if the algorithm were to have output 0.1, if it thinks that there's only a 10% chance of the tumor being malignant, but y really is one, it really is malignant, then the loss ... | [0.009430117905139923, -0.003429734380915761, 0.024820968508720398, -0.005653444677591324, 0.0008574335952289402, -0.016917379572987556, 0.00884197372943163, 0.01289950218051672, -0.024080833420157433, -0.021503575146198273, 0.0025309985503554344, 0.059686630964279175, -0.006112725008279085, 0.007645862642675638, -0.02... |
3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=E-ZcnndnY2I | that this function is convex is beyond the scope of this course. You may remember that the cost function is a function of the entire training set and is therefore the average or one over m times the sum of the loss function on the individual training examples. So the cost on a certain set of parameters w and b is equal... | 283 | 3.4 Cost function | Cost function for logistic regression --[Machine Learning | Andrew Ng]: that this function is convex is beyond the scope of this course. You may remember that the cost function is a function of the entire training set and is therefore the average or one over m times the sum of the loss function on ... | [0.005793314427137375, 0.0024031526409089565, 0.026342248544096947, -0.01004834659397602, -0.00518262293189764, -0.013032481074333191, 0.0321916826069355, 0.010220000520348549, -0.024295607581734657, -0.017231395468115807, -0.006097009405493736, 0.044814832508563995, -0.01577894203364849, 0.005770206917077303, -0.01366... |
3.5 Cost Function | Simplified Cost Function for Logistic Regression --[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=wB08Jlmhi24 | In the last video, you saw the loss function and the cost function for logistic regression. In this video, you'll see a slightly simpler way to write out the loss and cost functions so that the implementation can be a bit simpler when we get to gradient descent for fitting the parameters of a logistic regression model.... | 500 | 3.5 Cost Function | Simplified Cost Function for Logistic Regression --[Machine Learning|Andrew Ng]: In the last video, you saw the loss function and the cost function for logistic regression. In this video, you'll see a slightly simpler way to write out the loss and cost functions so that the implementation can be a b... | [-0.0006287122960202396, 0.009750471450388432, 0.03308080509305, -0.013245194219052792, -0.006387684959918261, -0.012524367310106754, 0.012588727287948132, 0.0013266111491248012, -0.01719686947762966, -0.021702038124203682, -0.0005506763700395823, 0.05648193508386612, -0.00883012916892767, -0.001218004385009408, -0.019... |
3.5 Cost Function | Simplified Cost Function for Logistic Regression --[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=wB08Jlmhi24 | loss from above, then it looks like this. 1 over m times the sum of this term above. And if you bring the negative signs and move them outside, then you end up with this expression over here. And this is the cost function. The cost function that pretty much everyone uses to train logistic regression. Now you might be w... | 275 | 3.5 Cost Function | Simplified Cost Function for Logistic Regression --[Machine Learning|Andrew Ng]: loss from above, then it looks like this. 1 over m times the sum of this term above. And if you bring the negative signs and move them outside, then you end up with this expression over here. And this is the cost functi... | [0.007680112961679697, 0.01290024071931839, 0.03273020312190056, -0.01687406376004219, -0.015112269669771194, -0.0028873831033706665, 0.011914941482245922, 0.00736038014292717, -0.020854409784078598, -0.022342147305607796, -0.004734003450721502, 0.03622768819332123, 0.004900394938886166, 0.006923194508999586, -0.017304... |
3.6 Gradient Descent | Gradient Descent Implementation --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=D59jK8T9dfI | To fit the parameters of a logistic regression model, we're going to try to find the values of the parameters w and b that minimise the cost function j of w and b. And we're going to apply gradient descent to do this. Let's take a look at how. In this video, we'll focus on how to find a good choice of the parameters w ... | 500 | 3.6 Gradient Descent | Gradient Descent Implementation --[Machine Learning | Andrew Ng]: To fit the parameters of a logistic regression model, we're going to try to find the values of the parameters w and b that minimise the cost function j of w and b. And we're going to apply gradient descent to do this. Let's take a... | [-0.0111823296174407, 0.011313040740787983, 0.01861325278878212, -0.010483025573194027, -0.00486571853980422, -0.0012090770760551095, 0.008411255665123463, 0.011090831831097603, -0.028756428509950638, -0.020482419058680534, 0.006215309724211693, 0.04130468890070915, -0.01615588366985321, 0.014940272085368633, -0.014508... |
3.6 Gradient Descent | Gradient Descent Implementation --[Machine Learning | Andrew Ng] | https://www.youtube.com/watch?v=D59jK8T9dfI | x is defined to be the sigmoid function applied to w x plus b. So although the algorithm written looked the same for both linear regression and logistic regression, actually they're two very different algorithms because the definition for f of x is not the same. When we talked about gradient descent for linear regressi... | 439 | 3.6 Gradient Descent | Gradient Descent Implementation --[Machine Learning | Andrew Ng]: x is defined to be the sigmoid function applied to w x plus b. So although the algorithm written looked the same for both linear regression and logistic regression, actually they're two very different algorithms because the defini... | [0.0005646232748404145, 0.016636047512292862, 0.018093978986144066, -0.01871880702674389, -0.0023740213364362717, 0.0031176316551864147, 0.015204149298369884, 0.004390067886561155, -0.030850885435938835, -0.025904329493641853, 0.008207377046346664, 0.036292098462581635, -0.014331993646919727, 0.008474230766296387, -0.0... |
3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=wBx3NZ0ucgc | Now you've seen a couple of different learning algorithms, linear regression and logistic regression. They work well for many tasks, but sometimes in an application the algorithm could run into a problem called overfitting, which can cause it to perform poorly. What I'd like to do in this video is to show you what is o... | 500 | 3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng]: Now you've seen a couple of different learning algorithms, linear regression and logistic regression. They work well for many tasks, but sometimes in an application the algorithm could run into a problem called overfitt... | [-0.003322847420349717, -0.009165145456790924, 0.029847782105207443, 0.00036514364182949066, -0.01057912316173315, -0.010109939612448215, 0.009435086511075497, -0.005167445167899132, -0.023523446172475815, -0.023292066529393196, 0.01073980238288641, 0.022675059735774994, -0.009107301011681557, 0.00028440231108106673, -... |
3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=wBx3NZ0ucgc | poorly, leading it to underfit the data. Now let's look at a second variation of a model, which is if you instead fit a quadratic function to the data with two features, x and x squared, then when you fit the parameters w1 and w2, you can get a curve that fits the data somewhat better. Maybe it looks like this. So if y... | 500 | 3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng]: poorly, leading it to underfit the data. Now let's look at a second variation of a model, which is if you instead fit a quadratic function to the data with two features, x and x squared, then when you fit the parameters... | [-0.005951606202870607, -0.00028289653710089624, 0.02267402596771717, 0.006514552049338818, -0.007471234537661076, -0.004191180225461721, 0.007607903331518173, -0.01064716000109911, -0.023376893252134323, -0.03139480575919151, 0.022010205313563347, 0.036158692091703415, -0.00823267549276352, -0.0028619123622775078, -0.... |
3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=wBx3NZ0ucgc | less, then the function that the algorithm fits could end up being totally different. So if two different machine learning engineers were to fit this fourth order polynomial model to just slightly different data sets, they could end up with totally different predictions or highly variable predictions. And that's why we... | 500 | 3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng]: less, then the function that the algorithm fits could end up being totally different. So if two different machine learning engineers were to fit this fourth order polynomial model to just slightly different data sets, t... | [-0.000809471239335835, 0.006074529141187668, 0.02996811456978321, 0.007959047332406044, -0.01965424232184887, -0.0010664135916158557, -0.0018910958897322416, -0.014378906227648258, -0.02648192085325718, -0.025455795228481293, 0.024692779406905174, 0.040439851582050323, -0.008426065556704998, -0.003139219246804714, -0.... |
3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=wBx3NZ0ucgc | terms, then z becomes this new term in the middle and the decision boundary, that is where z equals zero, can look more like this, more like an ellipse or part of an ellipse. And this is a pretty good fit to the data, even though it does not perfectly classify every single training sample in the training set. Notice ho... | 290 | 3.7 Regularization to Reduce Overfitting | The problem of Overfitting -[Machine Learning|Andrew Ng]: terms, then z becomes this new term in the middle and the decision boundary, that is where z equals zero, can look more like this, more like an ellipse or part of an ellipse. And this is a pretty good fit to the data, e... | [-0.00022822344908490777, -0.005961090326309204, 0.032339729368686676, 0.00928366556763649, -0.0178116075694561, 0.015218696556985378, 0.0016278988914564252, -0.004355179145932198, -0.02521248161792755, -0.024183133617043495, 0.025368837639689445, 0.02635909430682659, -0.003417040454223752, 0.004811218939721584, -0.004... |
3.8 Regularization to Reduce Overfitting | Addressing Overfitting-[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=ce4CPW8AFE4 | Later in this specialization, we'll talk about debugging and diagnosing things that can go wrong with learning algorithms. You also learn about specific tools to recognize when overfitting and underfitting may be occurring. But for now, when you think overfitting has occurred, let's talk about what you can do to addres... | 500 | 3.8 Regularization to Reduce Overfitting | Addressing Overfitting-[Machine Learning|Andrew Ng]: Later in this specialization, we'll talk about debugging and diagnosing things that can go wrong with learning algorithms. You also learn about specific tools to recognize when overfitting and underfitting may be occurring. ... | [-0.005359225906431675, -0.0001970364828594029, 0.035359565168619156, -0.011457884684205055, -0.010212172754108906, 0.013489660806953907, 0.009126338176429272, -0.020890654996037483, -0.02262265980243683, -0.0214369036257267, 0.02917763590812683, 0.029071051627397537, -0.0029544022399932146, -0.0076274871826171875, -0.... |
3.8 Regularization to Reduce Overfitting | Addressing Overfitting-[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=ce4CPW8AFE4 | the information that you have about the houses. For example, maybe all of these features, or 100 of them, are actually useful for predicting the price of a house. So maybe you don't want to throw away some of the information by throwing away some of the features. Later in course two, you also see some algorithms for au... | 500 | 3.8 Regularization to Reduce Overfitting | Addressing Overfitting-[Machine Learning|Andrew Ng]: the information that you have about the houses. For example, maybe all of these features, or 100 of them, are actually useful for predicting the price of a house. So maybe you don't want to throw away some of the information... | [-0.004623548127710819, 0.003359347116202116, 0.036496929824352264, -0.0006135092698968947, -0.013055865652859211, 0.0049598063342273235, 0.01620505191385746, -0.02746969647705555, -0.026163462549448013, -0.025736672803759575, 0.03204797953367233, 0.030133893713355064, -0.002531635109335184, 0.001430713338777423, -0.00... |
3.8 Regularization to Reduce Overfitting | Addressing Overfitting-[Machine Learning|Andrew Ng] | https://www.youtube.com/watch?v=ce4CPW8AFE4 | for training learning algorithms, including neural networks specifically, which you see later in the specialization as well. I hope you also check out the optional lab on overfitting. In the lab, you will see different examples of overfitting and adjust those examples by clicking on options in the plots. You also be ab... | 266 | 3.8 Regularization to Reduce Overfitting | Addressing Overfitting-[Machine Learning|Andrew Ng]: for training learning algorithms, including neural networks specifically, which you see later in the specialization as well. I hope you also check out the optional lab on overfitting. In the lab, you will see different examp... | [0.0014344857772812247, -0.014154468663036823, 0.044432949274778366, -0.014482726342976093, -0.007746879942715168, 9.498954750597477e-05, 0.014377683401107788, -0.019656065851449966, -0.03474278748035431, -0.013708038255572319, 0.02970074862241745, 0.02169126272201538, -0.008744782768189907, 0.006519196089357138, -0.00... |
3.9 Regularization to Reduce Overfitting | Cost function with regularization- [ML | Andrew Ng] | https://www.youtube.com/watch?v=SCj3h47dKL0 | In the last video, we saw that regularization tries to make the parameter values w1 through wn small to reduce overfitting. In this video, we'll build on that intuition and develop a modified cost function for your learning algorithm they can use to actually apply regularization. Let's jump in. Recall this example from... | 500 | 3.9 Regularization to Reduce Overfitting | Cost function with regularization- [ML | Andrew Ng]: In the last video, we saw that regularization tries to make the parameter values w1 through wn small to reduce overfitting. In this video, we'll build on that intuition and develop a modified cost function for your learning ... | [0.006411239970475435, -0.005553537979722023, 0.023167891427874565, 0.01887606829404831, -0.009716208092868328, -0.01005399040877819, 0.018491923809051514, -0.007305369712412357, -0.014703463762998581, -0.02593638002872467, 0.017299750819802284, 0.032162170857191086, -0.000823344336822629, 0.004639538936316967, -0.0042... |
3.9 Regularization to Reduce Overfitting | Cost function with regularization- [ML | Andrew Ng] | https://www.youtube.com/watch?v=SCj3h47dKL0 | less prone to overfitting. So for this example, if you have data with 100 features for each house, it may be hard to pick in advance which features to include and which ones to exclude. So let's build a model that uses all 100 features. So you have these 100 parameters, w1 through w100, as well as the 101st parameter b... | 500 | 3.9 Regularization to Reduce Overfitting | Cost function with regularization- [ML | Andrew Ng]: less prone to overfitting. So for this example, if you have data with 100 features for each house, it may be hard to pick in advance which features to include and which ones to exclude. So let's build a model that uses all 1... | [0.007294139824807644, -0.0030773186590522528, 0.019093288108706474, 0.010407895781099796, -0.01160702295601368, -0.014151028357446194, 0.016946783289313316, -0.00157923751976341, -0.031296562403440475, -0.03413206711411476, -0.004809759557247162, 0.028805557638406754, -0.012965151108801365, 0.004233383573591709, -0.00... |
3.9 Regularization to Reduce Overfitting | Cost function with regularization- [ML | Andrew Ng] | https://www.youtube.com/watch?v=SCj3h47dKL0 | x is the linear regression model. If lambda was set to be zero, then you're not using the regularization term at all, because the regularization term is multiplied by zero. And so if lambda was zero, you end up fitting this overly wiggly, overly complex curve, and it overfits. So that was one extreme of if lambda was z... | 341 | 3.9 Regularization to Reduce Overfitting | Cost function with regularization- [ML | Andrew Ng]: x is the linear regression model. If lambda was set to be zero, then you're not using the regularization term at all, because the regularization term is multiplied by zero. And so if lambda was zero, you end up fitting this ... | [0.009957320056855679, -0.0018796981312334538, 0.016086414456367493, 0.015391564927995205, -0.019337782636284828, -0.006007823161780834, 0.014447618275880814, -0.005883274599909782, -0.024870356544852257, -0.02808239497244358, 0.0064797960221767426, 0.024831024929881096, -0.011445345357060432, 0.019036244601011276, 0.0... |
3.10 Regularization to Reduce Overfitting | Regularized linear regression-- [ML | Andrew Ng] | https://www.youtube.com/watch?v=yRSKygmsvSI | In this video, we'll figure out how to get gradient descent to work with regularized linear regression. Let's jump in. Here's the cost function we've come up with in the last video for regularized linear regression. The first part is the usual squared error cost function. And now you have this additional regularization... | 500 | 3.10 Regularization to Reduce Overfitting | Regularized linear regression-- [ML | Andrew Ng]: In this video, we'll figure out how to get gradient descent to work with regularized linear regression. Let's jump in. Here's the cost function we've come up with in the last video for regularized linear regression. The first ... | [0.006366174202412367, -0.008871275000274181, 0.02210267446935177, -0.00031252202461473644, -0.005305692553520203, -0.017138436436653137, 0.016048405319452286, -0.01159635279327631, -0.026764854788780212, -0.02954902872443199, 0.009652682580053806, 0.022063275799155235, -0.013684484176337719, 0.013270797207951546, -0.0... |
3.10 Regularization to Reduce Overfitting | Regularized linear regression-- [ML | Andrew Ng] | https://www.youtube.com/watch?v=yRSKygmsvSI | can build a deeper intuition about what the math and what the derivatives are doing as well. So let's take a look. Let's look at the update rule for wj and rewrite it in another way. We're updating wj as one times wj minus alpha times lambda over m times wj. So I've moved the term from the end to the front here. And th... | 500 | 3.10 Regularization to Reduce Overfitting | Regularized linear regression-- [ML | Andrew Ng]: can build a deeper intuition about what the math and what the derivatives are doing as well. So let's take a look. Let's look at the update rule for wj and rewrite it in another way. We're updating wj as one times wj minus alp... | [0.0002191771345678717, -0.013717910274863243, 0.023155419155955315, -0.0010636537335813046, -0.01579364575445652, -0.008341623470187187, 0.006974990013986826, -0.01641250029206276, -0.029421307146549225, -0.0461045540869236, 0.009901649318635464, 0.023967664688825607, -0.01669614017009735, 0.02787417359650135, -0.0022... |
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