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SLAM_Lectures | SLAM_D_14.txt | now let's have a look at where we are so our prediction step was as follows our mu was computed by G and our Sigma prediction was computed by G Sigma G transposed plus our system noise R and so we have set up this equation and we also computed G which was the derivative with respect to the state so we know how to do th... |
SLAM_Lectures | SLAM_D_06.txt | and so the answer to this is our covariance matrix is Sigma x squared times 1 2 2 4 and we found out that this is Sigma x squared times the matrix made from our eigenvectors which need to be normalized times the diagonal matrix with the two eigenvalues time sweetie and we found out that lambda 1 was 5 lambda 2 was 0 so... |
SLAM_Lectures | SLAM_C_10.txt | now congratulations if you managed to program this correctly and if you did so you should see the following and this is a really amazing result so we started with our discrete distribution having to compute all those convolutions and multiplications value by value with all those steps and now we also implemented this u... |
SLAM_Lectures | SLAM_G_02.txt | now there's something interesting about data Association or Landmark correspondence now the basic situation is a robot moves along it does some measurements and based on this initializes some Landmark positions with some uncertainty then later on it measures the same landmarks and Associates the new measurements with t... |
SLAM_Lectures | SLAM_D_03.txt | and so interestingly different solutions are correct if I tell you that X is normal distributed with mu and sigma square and Y is normal distributed with 2 mu and sigma square and first of all you know that c which is also normal distributed has a mu Vector which is Mu and 2 mu so one thing you know for sure is that th... |
SLAM_Lectures | SLAM_E_01.txt | now welcome to Unit E which will be about the particle filter now let's first have a look at our base filter again which takes our old belief the control and our measurement and from that first computes a prediction which I will now give in a continuous form and then the correction which is our predicted belief multipl... |
SLAM_Lectures | SLAM_G_01.txt | now welcome to unit G of our slam lecture and this will be about particle filter slam and the particular algorithm that we will talk about here is also known as fast slam now to put that into perspective let's have a look at what we did so far so we started with the following problem a robot was placed in an arena with... |
SLAM_Lectures | SLAM_A_08.txt | there's one more thing I'll ask you to do but fortunately that is really really easy so we now do have two robot and the robots coordinate system is like this and as I told you earlier the beams go like that and back here is an area where no measurements are returned now what you just computed is terrain indices that p... |
SLAM_Lectures | SLAM_A_01.txt | so welcome to Unit A of our slam lecture and in this lecture we will get you started to work with a real robot so if you have taken the artificial intelligence course of Sebastian thron you now know about a self-driving car so here is one from the official Google block and what you can see here well that's just a stand... |
SLAM_Lectures | SLAM_D_15.txt | now we got everything we need to implement the prediction step so once again these are the two equations and redefined RT to be V times sigma control times we transposed well the signal control waltz the variance in the left control and the variance in the right control put into a diagonal matrix and now we still have ... |
SLAM_Lectures | SLAM_A_05.txt | so now that you programmed the position tracking I'll ask you for three more modifications so first of all as you remember we had some colored cardboard on top of our laser scanner and this license cannon was then tracked by our video tracker and you probably remember this red circle which try to follow the laser scann... |
SLAM_Lectures | SLAM_E_02.txt | and of course in general if I do not know anything about position of my robot then a uniform distribution will be the proper representation and so my belief will be constant well the constant is chosen such that the integral over my entire Arena will be one now let's introduce three important classes of localization pr... |
SLAM_Lectures | SLAM_D_01.txt | and welcome to unit D which will be about the Kalman filter let's first have a look what we will cover in this unit now if you remember in unit B we had the following we used our motor tics and emotion model to determine the robots position at any point in time and the result looked like that it was a pretty smooth tra... |
SLAM_Lectures | SLAM_E_07.txt | now after you run this it will produce the particle filter mean. text file let's load this and now what you see here is in green all the particles for step number zero and in blue the current mean position and mean heading and as we cycle through the trajectory we see how in every step the mean is computed which gives ... |
SLAM_Lectures | SLAM_C_09.txt | and it turns out that indeed the result is normal distributed so we obtain our prediction MW overline and sigma t² overline so this is our belief overline and the computation that leads us there is somehow tedious and so if you want you can look it up for example in the probabilistic robotics book by thr borgard and fo... |
SLAM_Lectures | SLAM_G_05.txt | so now let's put everything together and we will do this in the slam 10e correction file so as usual it starts with our particle class where each particle consists of a pose and one position and covariance entry for every landmark and then we have the function g for the state transition and the move function which we u... |
SLAM_Lectures | PP_11.txt | now let's have a look at how these few modifications actually improve the performance of the code a lot so let's place our start Point here and our end point here in the opposite direction as we did earlier and we can see that it comes up immediately with a solution and as we move this point closer it does the same thi... |
SLAM_Lectures | SLAM_C_01.txt | so welcome to Unit C which is about filtering so as you remember we started by looking at two trajectories the red one which we obtained by an additional external measurement system and not by the sensors that are available for the robot itself and the blue one which we obtained by reading out the wheel counters of the... |
SLAM_Lectures | SLAM_B_03.txt | so congratulations if you manage to program this SDM a transform function so this is non-trivial code and it's a really useful function which is able to compute the best similarity transform in a least square sense between a left point list and the right point list and I forgot some strange errors when the number of po... |
SLAM_Lectures | SLAM_F_05.txt | so so far our result was not so impressive because the implementation was made to handle an arbitrary number of landmarks but we never added one so now we'll have a look at how to add a landmark to our system State now this addition of landmarks will happen later on whenever the robot observes an object in the measurem... |
SLAM_Lectures | SLAM_D_04.txt | let's find this out looking for the tolerance matrix which is the expected value of C minus the expected value of C times C minus the expected value of C transpose and so C is as we have defined just X Y which is by the construction of our lever arm X and 2x so this is because our lever arm looked like that here was ou... |
SLAM_Lectures | PP_03.txt | so now that you implemented your first version of D algorithm let's make our first Improvement and this will be just a cosmetic Improvement and not an improvement of the algorithm as such so let me show you the outcome so if in this improved version I set the start and the goal it will do rooting exactly as before howe... |
SLAM_Lectures | SLAM_C_04.txt | now before we go on let's again have a look at the output so if you run the program we got the following result and the initial distribution is unit pulse which may be seen as a special discrete form of our triangle distribution with a halfwidth of one and the outcome of the first convolution that's a triangular distri... |
SLAM_Lectures | SLAM_A_04.txt | so I want you to implement this motion model and for that I have prepared some code for you so the code consists of a main function and the filter step and you will have to implement this filter step the filter step gets the old pose which consists of x y and the heading and it gets the motor ticks left and right and i... |
SLAM_Lectures | PP_09.txt | now while we achieved the pretty good path planning globally we still have to admit that locally the generated path is not drivable by a normal car so for example for this situation we may get a path like this which could be driven by a robot for example our two track robot which we used and the slam lecture which woul... |
SLAM_Lectures | SLAM_D_07.txt | okay then what is the dimension of B |
SLAM_Lectures | SLAM_D_17.txt | now it's time to produce our final result the extended calman filter and you've seen all the equations and several parts of this unit but let me put those together on one slide so we have the prediction step where we compute our predicted mu and our predicted Sigma so this is the predicted State and the predicted co-va... |
SLAM_Lectures | SLAM_G_03.txt | now let's have a look at Part B where our task is to initialize a new Landmark so the situation is our robot is here it measures a distance and bearing angle so that's our measurement C to a cylinder and it determines by Computing the likelihoods that this is a new Landmark that should be incorporated into its list of ... |
SLAM_Lectures | SLAM_A_06.txt | now let's talk about sensor data why do we need sensor data at all so if we now look at our solution we could be happy with it so it's pretty smooth but we don't know if it is correct so in order to find that out let us load the reference trajectory and this is called robot 4 reference. text now let's look at this so i... |
SLAM_Lectures | SLAM_A_09.txt | now finally I'll show you what you just programmed so open up the log file viewer and look for the text files so we'll start by loading the scan so we had a look at the scan before and you can travel using the slider through all scan measurements but now let's load those cylinders as well to the last program you've wri... |
SLAM_Lectures | PP_04.txt | now after this Improvement of the visualization let us now make some real algorithmic Improvement so as you noticed already the algorithm is pretty slow so what's the reason for this so as you remember we start from a note and then we expand all the nodes around it so at some stage of the algorithm we do have this set ... |
SLAM_Lectures | SLAM_C_08.txt | and to answer that let's have a look at the density of the normal distribution which is also termed the Gaussian distribution and which is defined as 1 divided by the square root of 2 pi times Sigma times e raised to the power of minus 1/2 times X minus mu divided by Sigma squared and this now is a density meaning the ... |
SLAM_Lectures | SLAM_F_07.txt | now let's have a closer look at the results so in the beginning our robot starts here at an arbitrary position and orientation and its uncertainty in position and heading is zero because we just defined that the map it will construct has its origin at the initial robot position and so let me just explain all the colors... |
SLAM_Lectures | SLAM_A_00.txt | so welcome to my class on simultaneous localization and mapping my name is Claus brener and I'm a professor at the liess University hover in Germany so what you see here is our small robot which will both fit with a laser scanner or lighter and which will run on the ground and detect landmarks such as this one and usin... |
SLAM_Lectures | SLAM_D_02.txt | now let's first have a look at the normal distribution again so we said X is normally distributed with the parameters mu and Sigma square if its density function was like that so there is a normalizer times e raised to the power of minus 1/2 times X minus mu squared divided by Sigma squared and so this was in 1d meanin... |
SLAM_Lectures | SLAM_D_10.txt | what is the dimension of Q the measurement noise |
SLAM_Lectures | PP_05.txt | so after you implemented this interaction with the user interface will actually become pretty cool so you set a start node you set endnote and very quickly it will come up with the set of all visited nodes and the user interface will react pretty quickly also it's now more fun to draw some obstacles and have a look at ... |
SLAM_Lectures | SLAM_A_07.txt | so this was really easy and here's my solution so all you have to do is you grab the left value you grab the right value and then if those values are both larger than the minimum distance then you compute the derivative which is actually the difference quotient and you appen this and if either one of those two is small... |
SLAM_Lectures | SLAM_C_02.txt | so in this last programming assignment is a true member of the set of shortest programming assignments in the world because it can be solved just one line of code so all you have to do is this now since this is our set of values of our distribution and we've chosen to store any finite subset by giving this subset and a... |
SLAM_Lectures | SLAM_E_05.txt | now that's the corrections that looked like now remember in the general formulation of the Bayes filter our new belief was computed by weighting our predicted belief by the probability of the measurement given the state and we had to normalize this when the product filter this is implemented as follows for every partic... |
SLAM_Lectures | SLAM_F_02.txt | now transfer this think about the following say there are some landmarks and you observed them from a certain position then essentially what we do is that we set up a link between the positions of our landmarks where we do not directly observe the distance between two landmarks but rather we have served them through an... |
SLAM_Lectures | SLAM_E_06.txt | now talking about the solution where is the robot actually so now we have all those particles but still in order to make some decisions we need to derive one or more actual states of the robot and this is called density estimation so for example if this is our world and here are our particles you could do a histogram o... |
SLAM_Lectures | SLAM_E_04.txt | now to see what happens remember how our calman filtering in the last unit worked so we had our landmarks and in every time step we extracted from our laser scan measurements the locations of landmarks which Were Somehow noisy but we used the assumed position and orientation of our robot to map those detected cylinders... |
SLAM_Lectures | SLAM_F_04.txt | now let's have a look at the extended kalman filter slan and fortunately we have implemented the extended kalman filter already so first let's have a look at the prediction step now as we know in the prediction we computed our new mu from the possibly non-linear function g which took the old mu and our control and then... |
SLAM_Lectures | SLAM_C_06.txt | now let's again have a look at the multiplication of two distributions so we had one distribution centered at 400 with a half worth of 100 and another one centered at 410 with a half worth of 200 meaning something less accurate and we multiplied these two and the outcome was as follows and so we already noted that even... |
SLAM_Lectures | SLAM_D_18.txt | so congratulations if you made it that far so you really developed some non-trivial code so starting from a real robot you modeled the motion and the measurement and derived all the derivative matrices that were necessary and then implemented the prediction and the correction step of a full-fledged Calon filter and so ... |
SLAM_Lectures | PP_02.txt | so dkar algorithm can be applied to any problem where we have graph of nodes connected by edges the edges have Associated costs or weights which are non- negative and we want to find a minimum cost path from a start Noe to a goal Noe and so although we imagine this to be a representation of a road Network while the cos... |
SLAM_Lectures | SLAM_D_08.txt | so what is the dimension of C |
SLAM_Lectures | SLAM_C_05.txt | so in order to answer that question let's do the math so what I'm interested in is the probability that I am in the position X now given that I did some measurement see right an according to Bayes rule that space this is the probability of having the measurement given that I'm in the position X so as you can see using ... |
SLAM_Lectures | SLAM_A_02.txt | now that we ran our robot through our Arena let's look at motor control so this is our robot here is our ligher the scanner which will scan somewhere in that range and we'll have an invisible area here and these are two Motors and the two Motors they drive these axes meaning if they go at a certain speed the robot will... |
SLAM_Lectures | PP_01.txt | now welcome to this lecture which covers some basics of path planning and you will learn about the algorithms of dtra AAR and finally a path planner that does trajectories which can be driven by a real car so before we start let me show you some of the results so there's a graphical user interface where you can explore... |
SLAM_Lectures | SLAM_D_12.txt | and finally for the computations we need the common gain what is the dimension of the common gain Matrix |
SLAM_Lectures | SLAM_F_06.txt | now let's have a look at the observations so our basic setup was as follows a robot stood somewhere with a certain Hattie so this is the robot's position as reflected by the state but the scanner has a certain offset and so if the heading angle is Theta the position of the laser will be XL yl in XL yl simply will be XY... |
SLAM_Lectures | SLAM_G_07.txt | now if you run your code and you're worried about the smoothness of the trajectory keep in mind that be used only 25 particles so if you don't like that modify this number which is said in the main function so here is a result which are obtained using 200 particles so as you can see the trajectory is now much much smoo... |
SLAM_Lectures | SLAM_B_02.txt | now after we have the assignment of points we're left with the following problem we have a left list of points which are the detected landmarks and we have a right list of points and we already know the assignment between those points we now want to find the transformation which maps all the points from the left list t... |
SLAM_Lectures | SLAM_F_08.txt | and there's no simple answer because as you've just learned if you have only a few then this means for the landmark assignment it is good because if there's only a few it's hard to confuse them on the other hand it means for the localization that if there are only a few landmarks we will have less observations so our a... |
SLAM_Lectures | PP_06.txt | we now have a pretty cool implementation of dykstra's algorithm so set the start set the goal now you will see not only always the nodes but also the actual path that is taken from start to go let's set a different goal and now let me show you something interesting say if you go from here to there then you have this to... |
SLAM_Lectures | SLAM_C_07.txt | so let's find that out let's define a function f ofx which equals e the IR constant raised to the power of -2 * x - mu this is a mean / Sigma and this is the standard deviation squared now if we plot this function we will have the following x - mu / Sigma is z independently of Sigma when X = mu so here you have x f of ... |
SLAM_Lectures | SLAM_D_05.txt | now let me ask you a second question so this is our degenerated Arrow ellipse and so we know this half axis here that is zero now I would like to know what is the extend in the other direction so is it Sigma X or is it Square < TK of 2 Sigma X or Square < TK of 4 Sigma X or Square < TK of 5 Sigma X so please choose the... |
Introduction_to_Robotics_Princeton | Lecture_8_Princeton_Introduction_to_Robotics_Randomized_motion_planning_RRTs.txt | all right I think we can go and get started so here's a quick reminder of uh the concept that we covered in the the past two lectures uh so the main uh I guess ideas set of ideas we've been covering the the past two lectures has to do with motion planning and specifically we'll be looking at motion planning in discrete... |
Introduction_to_Robotics_Princeton | Lecture_22_Princeton_Introduction_to_Robotics_Convolutional_neural_networks.txt | all right let's go ahead and get started um so we're gonna continue our discussion of uh of deep learning so today's gonna be the last lecture on supervised learning and the next lecture I guess based on the the poll that we took uh is going to be on reinforcement learning uh so in the previous lecture we uh continued ... |
Introduction_to_Robotics_Princeton | Lecture_15_Princeton_Introduction_to_Robotics_Mapping.txt | all right yeah let's go and get started so the the plan for this week so today's lecture and also Thursday's lecture is to wrap up this module that we've been focusing on uh of uh localization mapping and state estimation uh so just to remind you uh of what we've been doing in the the last uh couple of lectures so the ... |
Introduction_to_Robotics_Princeton | Lecture_7_Princeton_Introduction_to_Robotics_Optimal_Discrete_Planning.txt | [Music] foreign so just a reminder of where we left things in the previous lecture so we started our discussion of motion planning and specifically we started talking about motion planning in discrete spaces so we thought about how we can take a continuous motion planning problem uh promotion planning in a continuous s... |
Introduction_to_Robotics_Princeton | Lecture_23_Princeton_Introduction_to_Robotics_Reinforcement_Learning.txt | all right Dad maybe we can go and get started so the plan for today is to uh to cover the enforcement learning so this is going to be basically the the last kind of technical topic that we cover in this course so in the previous lecture we talked about imitation learning uh which is this idea that you collect some demo... |
Introduction_to_Robotics_Princeton | Lecture_21_Princeton_Introduction_to_Robotics_Overfitting_and_regularization.txt | all right I think we'll go ahead and get started so welcome back from the the Brick uh hopefully you had a nice break relaxing uh rub the home stretch it's in the previous lecture we started uh talking about how we can train a single layer neural networks uh we uh I guess a couple of variants of of gradient descent so ... |
Introduction_to_Robotics_Princeton | Lecture_20_Princeton_Introduction_to_Robotics_Stochastic_gradient_descent.txt | all right there let's go and get started so this is a reminder in the previous lecture we started discussing learning based approaches I guess mostly to the computer vision but as we'll see many of the techniques that we discussed will also be really relevant for other kinds of learning beyond computer vision so it see... |
Introduction_to_Robotics_Princeton | Lecture_1_Princeton_Introduction_to_Robotics.txt | all right i think we're gonna go ahead and get started so here's the the plan for for the day so welcome to lecture one of uh intro to robotics mae 345 549. here's the the agenda for the day so we're gonna start by talking a little bit about some logistics for the the course so assignments uh projects and and so on uh ... |
Introduction_to_Robotics_Princeton | Lecture_16_Princeton_Introduction_to_Robotics_SLAM.txt | all right let's go and get started so the plans for the day is to finish up this module that we'll be looking at for the last few lectures on localization and mapping uh and just to remind me of uh where we are so far so in the last two lectures we've been looking at uh applications of Base filtering to the problem of ... |
Introduction_to_Robotics_Princeton | Lecture_11_Princeton_Introduction_to_Robotics_The_Nondeterministic_Filter.txt | [Music] but before we do that let's just take talk of what we've discussed so far uh what we're able to achieve with the concepts that we've introduced and what we still need to do so so far in this course we've basically discussed two main topics so the first one was feedback control where we set for ourselves the the... |
Introduction_to_Robotics_Princeton | Lecture_17_Princeton_Introduction_to_Robotics_Intro_to_Vision.txt | all right yeah let's go and get started uh it's super quiet I'm just quiet okay so we're gonna start off with a new uh topic today uh so computer vision and this is going to be the last uh major kind of module for the course originally combined with uh with machine learning uh so this is motivation uh since the last mo... |
Introduction_to_Robotics_Princeton | Lecture_19_Princeton_Introduction_to_Robotics_Intro_to_deep_learning_for_vision.txt | all right Dad maybe we can go and get started so the the plan for for today is to continue our discussion of computer vision so just to remind you in the previous lecture uh we discussed Optical flow uh which is the the apparent motion of objects as they appear in some kind of sequence of images that a camera is captur... |
Introduction_to_Robotics_Princeton | Lecture_12_Princeton_Introduction_to_Robotics_Bayes_Filtering.txt | all right now I think we'll go ahead and get started so just to remind you of uh where we left off in the the previous lecture so we started discussing a new topic uh which is the topic of State estimation so basically how can a robot use its potentially imperfect sensors to get a decent estimate of a state which you c... |
Introduction_to_Robotics_Princeton | Lecture_5_Princeton_Introduction_to_Robotics_Linear_Quadratic_Regulator_LQR.txt | okay uh let's go ahead and get started uh so just a quick reminder that the first assignment is due tomorrow by midnight and then the first Hardware lab component assignment is going to be assigned tomorrow and will be due uh the Wednesday after I'll say more about the logistics for the lab uh tomorrow 11 59 yeah yeah ... |
Introduction_to_Robotics_Princeton | Lecture_4_Princeton_Introduction_to_Robotics_Stability_and_PD_Control.txt | all right i think we can uh go and get started so i'll try to end today's lecture about uh just a few minutes early maybe five seven ten minutes early uh to hand out drones uh at the end of the lecture but uh yeah i guess for now we're just gonna continue with our technical materials that we started off in in the previ... |
Introduction_to_Robotics_Princeton | Lecture_9_Princeton_Introduction_to_Robotics_Differential_flatness.txt | all right let's go and get started so I'll start off with a reminder of what we covered in the previous lecture so we're continuing our discussion of motion planning and specifically emotion planning in continuous spaces so in the previous lecture we discussed randomized algorithms for planning and the main algorithm t... |
Introduction_to_Robotics_Princeton | Lecture_6_Princeton_Introduction_to_Robotics_Discrete_Planning_BFS_and_DFS.txt | all right I think we can uh go and get started so the plan for today is actually to start a new topic which is motion planning but before I do that I just wanted to show you a couple of the videos that I couldn't show uh in the at the end of the last lecture uh with lq art so here's a video of a drone with a lqr contro... |
Introduction_to_Robotics_Princeton | Lecture_2_Princeton_Introduction_to_Robotics_Dynamics.txt | [Music] foreign I think we're gonna go ahead and get started so the the plan for today is to start off with the the main kind of tactical meet for this course so as I mentioned in the the previous lecture uh the the main kind of motivating example that we're going to use as a unifying team throughout this course uh is ... |
Introduction_to_Robotics_Princeton | Lecture_3_Princeton_Introduction_to_Robotics_Feedback_Control.txt | all right I think we can uh go and get started so just a couple of uh quick logistic logistical announcements so the first problem set is going to go out uh tomorrow which is Wednesday and it's going to be due one week later so the Wednesday after uh at midnight and you'll submit it uh through greatscope we'll have ins... |
Introduction_to_Robotics_Princeton | Lecture_24_Princeton_Introduction_to_Robotics_Robotics_and_the_economy_ethics_and_laws.txt | all right maybe we can go and get started so welcome to the the last uh lecture of the semester so here's the the plan for uh for the day uh so I'm going to mention some uh topics some technical topics that I haven't really covered or haven't done Justice to uh the next thing we'll do is zoom out a little bit uh and ta... |
Introduction_to_Robotics_Princeton | Lecture_14_Princeton_Introduction_to_Robotics_Localization.txt | all right Dad let's go and get started so in the last two lectures we've been looking at this concept of Base filtering and we focused on the problem of State estimation using using waste filtering uh so what we did two lectures ago was discussed the general form of the the base filter which kind of followed on from ou... |
Introduction_to_Robotics_Princeton | Lecture_13_Princeton_Introduction_to_Robotics_Particle_filters_and_Kalman_filters.txt | all right that maybe we can go ahead and get started so yeah hopefully you had a good break uh we're gonna start off pretty much where we left off in the the previous lecture so we're covering this topic of uh State estimation localization and mapping uh and in the the previous lecture we discussed this kind of General... |
Introduction_to_Robotics_Princeton | Lecture_18_Princeton_Introduction_to_Robotics_Optical_Flow.txt | pretty much all right let's go and get started so the the plan for today as I mentioned in the previous lectures to spend the whole lecture today talking about the optical flow so this is one particular problem in computer vision that has like particular relevance to robotics as I'll explain in a bit um so I guess here... |
Introduction_to_Robotics_Princeton | Lecture_10_Princeton_Introduction_to_Robotics_Planning_with_dynamics_constraints.txt | all right uh let's go and get started so today is going to be the the last lecture on uh the small deal on motion planning so just to remind you of where we left off in the previous lecture uh so we started discussing motion planning with Dynamics constraints thank you so our first boss at the rrt algorithm we didn't t... |
Ted_Ed_Egyptian_History | A_day_in_the_life_of_an_ancient_Egyptian_doctor_Elizabeth_Cox.txt | It’s another sweltering morning in Memphis, Egypt. As the sunlight brightens the Nile, Peseshet checks her supplies. Honey, garlic, cumin, acacia leaves, cedar oil. She’s well stocked with the essentials she needs to treat her patients. Peseshet is a swnw, or a doctor. In order to become one, she had to train as a s... |
Ted_Ed_Egyptian_History | The_Egyptian_myth_of_the_death_of_Osiris_Alex_Gendler.txt | It was a feast like Egypt had never seen before. The warrior god Set and his wife, the goddess Nephtys, decorated an extravagant hall for the occasion, with a beautiful wooden chest as the centerpiece. They invited all the most important gods, dozens of lesser deities, and foreign monarchs. But no one caused as big... |
Ted_Ed_Egyptian_History | The_Egyptian_myth_of_Isis_and_the_seven_scorpions_Alex_Gendler.txt | A woman in rags emerged from the swamp flanked by seven giant scorpions. Carrying a baby, she headed for the nearest village to beg for food. She approached a magnificent mansion, but the mistress of the house took one look at her grimy clothes and unusual companions and slammed the door in her face. So she continue... |
Ted_Ed_Egyptian_History | 미라를_만드는_법ㅣ렌_블로치_Len_Bloch.txt | Death and taxes are famously inevitable, but what about decomposition? As anyone who's seen a mummy knows, ancient Egyptians went to a lot of trouble to evade decomposition. So, how successful were they? Living cells constantly renew themselves. Specialized enzymes decompose old structures, and the raw materials are ... |
Ted_Ed_Egyptian_History | How_did_they_build_the_Great_Pyramid_of_Giza_Soraya_Field_Fiorio.txt | As soon as Pharaoh Khufu ascended the throne circa 2575 BCE, work on his eternal resting place began. The structure’s architect, Hemiunu, determined he would need 20 years to finish the royal tomb. But what he could not predict was that this monument would remain the world’s tallest manmade structure for over 3,800 yea... |
Ted_Ed_Egyptian_History | The_Egyptian_Book_of_the_Dead_A_guidebook_for_the_underworld_Tejal_Gala.txt | Ani stands before a large golden scale where the jackal-headed god Anubis is weighing his heart against a pure ostrich feather. Ani was a real person, a scribe from the Egyptian city of Thebes who lived in the 13th century BCE. And depicted here is a scene from his Book of the Dead, a 78-foot papyrus scroll designed to... |
AI_LLM_Stanford_CS229 | Stanford_CS224N_NLP_with_Deep_Learning_Winter_2019_Lecture_14_Transformers_and_SelfAttention.txt | Okay. So I'm delighted to introduce, um, our first lot of invited speakers. And so we're gonna have two invited speakers, um, today. So starting off, um, we go and have Ashish Vaswani who's gonna be talking about self attention for generative models and in particular, um, we'll introduce some of the work on transformer... |
AI_LLM_Stanford_CS229 | How_to_Prepare_for_a_Product_Manager_Interview.txt | Hey, I'm Priyanka. And today I'm going to give you some tips on how to prepare for your product manager interview. As with all interviews, it's important to prepare in advance. Depending on the company, industry, and the level that you're applying for, you will be asked to demonstrate your knowledge and experience in o... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_5_Rademacher_complexity_empirical_Rademacher_complexity.txt | So I guess, yeah, sorry for the delay a little bit. I couldn't find water somehow. Anyway, so but OK, let's get started. So last time we talked about concentration equality, which was for some preparations for what we need today or maybe the next lecture. And today, we are going to go back to the uniform convergence. S... |
AI_LLM_Stanford_CS229 | Stanford_CS330_I_Advanced_MetaLearning_TopicsTask_Construction_l_2022_I_Lecture_9.txt | Cool. So now that we're at the start of week five of the quarter, I'd figured I'd give a little bit of a roadmap. So far we've seen multi-task learning and transfer learning basics. And then we covered some of the core meta learning algorithms. And last week we covered core unsupervised pre-training algorithms. And rea... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_3_Finite_hypothesis_class_discretizing_infinite_hypothesis_space.txt | OK, now, let's talk about math. So last time, where we ended was, we were talking about uniform convergence. So we said that our goal for the next few lectures will be the so-called uniform convergence, which means that you want to somewhat prove that with high probability, if you take sup on maximum-- like a sup reall... |
AI_LLM_Stanford_CS229 | The_End_of_Finetuning_with_Jeremy_Howard_of_Fastai.txt | Everyone, welcome to the latest space podcast. This is Alessio, partner and CTO in residence at Decibel Partners. And I'm joined by my co-host, Swyx, founder of Smol AI. Hey, and today we have in the remote studio Jeremy Howard from all the way from Australia. Good morning. The remote studio also known as my house. Goo... |
AI_LLM_Stanford_CS229 | Andrew_Ng_Opportunities_in_AI_2023.txt | [MUSIC PLAYING] It is my pleasure to welcome Dr. Andrew Ng, tonight. Andrew is the managing general partner of AI Fund, founder of DeepLearning.AI and Landing AI, chairman and co-founder of Coursera, and an adjunct professor of Computer Science, here at Stanford. Previously, he had started and led the Google Brain team... |
AI_LLM_Stanford_CS229 | Run_ANY_OpenSource_Model_LOCALLY_LM_Studio_Tutorial.txt | this is the easiest way to get open-source large language models running on your local computer it doesn't matter if you've never experimented with AI before you can get this working the software is called LM Studio something that I've used in previous videos and today I'm going to show you how to use it let's go this ... |
AI_LLM_Stanford_CS229 | Stanford_CS229M_Lecture_14_Neural_Tangent_Kernel_Implicit_regularization_of_gradient_descent.txt | OK. Hello, everyone. Let's get started. So last time, what we did was the NTK, the neural tangent kernel approach. And so today, we're going to continue with that to finish the last part of the neural tangent kernel approach. And then we talk about the so-called implicit regularization effect. So the last time, briefly... |
AI_LLM_Stanford_CS229 | Stanford_CS330_I_Variational_Inference_and_Generative_Models_l_2022_I_Lecture_11.txt | So for this week, we are going to be talking about a Bayesian perspective on meta-learning. And today's lecture is going to be a little bit different than a lot of the lectures we've had so far because we're really not going to be talking that much about meta-learning in this lecture, we're going to be talking about do... |
AI_LLM_Stanford_CS229 | The_Future_of_AI_is_Here_FeiFei_Li_Unveils_the_Next_Frontier_of_AI.txt | visual spatial intelligence is so fundamental it's as fundamental as language we've got this ingredients compute deeper understanding of data and we've got some advancement of algorithms we are in the right moment to really make a bet and to focus and just unlock [Music] that over the last two years we've seen this kin... |
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