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Speaker 1

00:00

Welcome everyone to 2019. It's really good to see everybody here, make it in the cold. This is 6S094,

Speaker 2

00:08

Deep Learning for Self-Driving Cars.

Speaker 3

00:13

It is part of a series of courses

Speaker 2

00:15

on deep learning that we're running throughout this month. The website that you can get all the content, the videos, the lectures, and the code is deeplearning.mit.edu. The videos and slides will be made available there, along with a GitHub repository that's accompanying the course.

Speaker 2

00:33

Assignments for registered students will be emailed later on in the week. And you can always contact us with questions, concerns, comments at hcaihumancenteredai.mit.edu. So let's start through the basics, the fundamentals. To summarize in 1 slide, what is deep learning?

Speaker 2

00:58

It is a way to extract useful patterns from data in an automated way was as little human effort involved as possible, hence the automated. How the fundamental aspect that we'll talk about a lot is the optimization of neural networks. The practical nature that we'll provide through the code and so on is that there's libraries that make it accessible and easy to do some of the most powerful things in deep learning using Python, TensorFlow, and Friends. The hard part always with machine learning and artificial intelligence in general is asking good questions and getting good data.

Speaker 2

01:49

A lot of times the exciting aspects of what's the news covers and a lot of the exciting aspects of what is published in the prestigious conferences and an archive and a blog post is the methodology. The hard part is applying that methodology to solve real world problems, to solve fascinating, interesting problems, and that requires data. That requires asking the right questions of that data, organizing that data, and labeling, selecting aspects of that data that can reveal the answers to the questions you ask. So why has this breakthrough over the past decade of the application of neural networks, the ideas of neural networks, what has happened, what has changed, have been around since the 1940s and ideas have been percolating even before.

Speaker 2

02:43

The digitization of information, data, the ability to access data easily in a distributed fashion across the world. All kinds of problems have now a digital form that can be accessed by learning algorithms. Hardware, compute, both the Moore's Law of CPU and GPU and ASICs, Google's TPU systems, hardware that enables the efficient, effective large-scale execution of these algorithms. Community, people here, people all over the world being able to work together, to talk to each other, to feed the fire of excitement behind machine learning.

Speaker 2

03:29

GitHub and beyond. The tooling, as we'll talk about TensorFlow, PyTorch, and everything in between, that enables a person with an idea to reach a solution in less and less and less time. Higher and higher levels of abstraction empower people to solve problems in less and less time with less and less knowledge, where the idea and the data become the central point, not the effort that takes you from idea to the solution. And there's been a lot of exciting progress, some of which we'll talk about, from face recognition to the general problem of scene understanding, image classification, to speech, text, natural language processing, transcription, translation, in medical applications and medical diagnosis, and cars being able to solve many aspects of perception in autonomous vehicles with drivable area lane detection, object detection, digital assistants, the ones on your phone and beyond, the ones in your home, ads, recommender systems from Netflix to Search to Social, Facebook, and of course, the deep reinforcement learning successes in the playing of games, from board games to Starcraft and Dota.

Speaker 3

04:58

Let's take a step back.

Speaker 2

05:01

Deep learning is more than a set of tools to solve practical problems. Pamela McCordick said in 79, AI began with the ancient wish to forge the gods. Throughout our history, throughout our civilization, human civilization, we've dreamed about creating echoes of whatever is in this mind of ours in the machine and creating living organisms.

Speaker 2

05:29

From the popular culture in the 1800s with Frankenstein to Ex Machina, this vision, this dream of understanding intelligence and creating intelligence has captivated all of us. And deep learning is at the core of that because there's aspects of it, the learning aspects, that captivate our imagination about what is possible, given data and methodology, what learning, learning to learn, and beyond, how far that can take us. And here visualized is just 3% of the neurons and 1 millionth of the synapses in our own brain. This incredible structure that's in our mind and there's only echoes of it, small shadows of it in our artificial neural networks that we're able to create.

Speaker 2

06:19

But nevertheless, those echoes are inspiring to us. The history of neural networks on this pale blue dot of ours started quite a while ago with summers and winters, with excitements and periods of pessimism, starting in the 40s with neural networks and the implementation of those neural networks as a perceptron in the 50s, with ideas of back propagation, restricted Boltzmann machines, recurring neural networks in the 70s and 80s, with convolutional neural networks, and the MNIST dataset, with datasets beginning to percolate, and LSTMs, bidirectional RNNs in the 90s, and the rebranding and the rebirth of neural networks under the flag of deep learning and deep belief nets in 2006. The birth of ImageNet, the data set that, on which the possibilities of what deep learning can bring to the world has been first illustrated in the recent years in 2009. And AlexNet, the network that on ImageNet performed exactly that with a few ideas like dropout that improve neural networks over time every year by year improving the performance of neural networks.

Speaker 2

07:39

In 2014, the idea of GANs that Yanle Kun called The most exciting idea of the last 20 years, the generative adversarial networks, the ability to, with very little supervision, generate data, to generate ideas after forming representation of those. From the understanding, from the high-level abstractions of what is extracted in the data, be able to generate new samples, create. The idea of being able to create as opposed to memorize is really exciting. And on the applied side, in 2014 with DeepFace, the ability to do face recognition.

Speaker 2

08:19

There's been a lot of breakthroughs on the computer vision front, that being 1 of them. The world was inspired, captivated in 2016 with AlphaGo and 17 with AlphaZero, beating with less and less and less effort the best players in the world at Go. The problem that for most of the history of artificial intelligence thought to be unsolvable. And new ideas with capsule networks, and this year is the year, 2018 was the year of natural language processing.

Speaker 2

08:55

A lot of interesting breakthroughs. Google's BERT and others that will talk about breakthroughs on ability to understand language, understand speech, and everything including generation that's built all around that. And there's a parallel history of tooling starting in the 60s with the Perceptron and the wiring diagrams. They're ending with this year with PyTorch 1.0 and TensorFlow 2.0.

Speaker 2

09:26

These really solidified, exciting, powerful ecosystems of tools that enable you to do very to do a lot with very little effort. The sky is the limit thanks to the tooling. So let's then from the big picture take into the smallest. Everything should be made as simple as possible.

Speaker 2

09:54

So let's start simple with a little piece of code before we jump into the details and a big run through everything that is possible in deep learning. At the very basic level with just a few lines of code, really 6 here, 6 little pieces of code, You can train a neural network to understand what's going on in an image. The classic that I will always love, MNIST dataset, the handwriting digits where the input to a neural network, a machine learning system, is the picture of a handwritten digit, and the output is the number that's in that digit. It's as simple as in the first step, import the library, TensorFlow.

Speaker 2

10:42

Second step, import the dataset, MNIST. Third step, like Lego bricks, stack on top of each other, the neural network, layer by layer, with a hidden layer, an input layer, an output layer. Step 4, train the model. As simple as a single line, model fit.

Speaker 2

11:03

Evaluate the model in step 5 on the testing data set, and that's it. In step 6, you're ready to deploy. You're ready to predict what's in the image. It's as simple as that.

Speaker 2

11:15

And much of this code, obviously much more complicated, or much more elaborate and rich and interesting and complex will be making available on GitHub on our repository that accompanies these courses. Today we've released the first tutorial on driver scene segmentation. I encourage everybody to go through it. And then on the tooling side, in 1 slide, before we dive into the neural networks and deep learning, The tooling side, amongst many other things, TensorFlow is a deep learning library, an open source library from Google.

Speaker 2

11:53

The most popular 1 to date, the most active with a large ecosystem. It's not just something you import in Python and to solve some basic problems. There's an entire ecosystem of tooling. There's different levels of APIs.

Speaker 2

12:10

Much of what we'll do in this course will be the highest level API with Keras. But there's also the ability to run in the browser with TensorFlow.js, on the phone with TensorFlow Lite, in the cloud, without any need to have a computer, hardware, anything, any

Speaker 3

12:27

of the libraries set up

Speaker 2

12:28

on your own machine, you can run all the code that we're providing in the cloud with Google Collaboratory, and the optimized ASICs hardware that Google has optimized for TensorFlow with their TPU, Tensor Processing Unit, ability to visualize TensorBoard, models that provide in TensorFlow Hub. And there's just an entire ecosystem, including, most importantly, I think, documentation and blogs that make it extremely accessible to understand the fundamentals of the tooling that allow you to solve the problems from natural language processing to computer vision, to GANs, generative adversarial neural networks, and everything in between, deep reinforcement learning and so on. So that's why we're excited to sort of work both in the theory in this course, in this series of lectures, and in the tooling and the applied side of TensorFlow.

Speaker 2

13:28

It really makes it exceptionally, these ideas exceptionally accessible. So deep learning at the core is the ability to form higher and higher level of abstractions of representations in data and raw patterns, higher and higher levels of understanding of patterns. And those representations are extremely important and effective for being able to interpret data. Under certain representations data is trivial to understand.

Speaker 2

14:06

Cat versus dog, blue dot versus green triangle. Under others it's much more difficult. In this task drawing a line under polar coordinates is trivial. Under Cartesian coordinates is very difficult, well impossible to do accurately.

Speaker 2

14:25

And that's a trivial example of a representation. So our task with deep learning, with machine learning in general, is forming representations that map the topology, whatever the topology, the rich space of the problem that you're trying to deal with of the raw inputs, map it in such a way that the final representation is trivial to work with, trivial to classify, trivial to perform regression, trivial to generate new samples of that data. And that representation of higher and higher levels of representation is really the dream of artificial intelligence. That is what understanding is, making the complex simple, like Einstein back in a few slides ago said.

Speaker 2

15:16

And that, with Juergen Schmidhuber and whoever else said it, I don't know, that's been the dream of all of science in general, of the history of science is the history of compression progress, of forming simpler and simpler representations of ideas. The models of the universe of our solar system with the earth at the center of it, is much more complex to perform, to do physics on than a model where the sun is at the center. Those higher and higher levels of simple representations enable us to do extremely powerful things. That has been the dream of science and the dream of artificial intelligence.

Speaker 2

16:08

And why deep learning? What is

Speaker 3

16:11

so special about deep learning in

Speaker 2

16:12

the grander world of machine learning and artificial intelligence? It's the ability to more and more remove the input of human experts, remove the human from the picture, the human costly inefficient effort of human beings in the picture. Deep learning automates much of the extraction from the raw, gets us closer and closer to the raw data without the need of human involvement, human expert involvement.

Speaker 2

16:40

Ability to form representations from the raw data as opposed to having a human being needing to extract features as was done in the 80s and 90s and the early aughts to extract features with which then the machine learning algorithms can work with. The automated extraction of features enables us to work with large and larger data sets removing the human completely except from the supervision labeling step at the very end. It doesn't require the human expert.

Speaker 3

17:10

But at the same time, there is

Speaker 2

17:16

limits to our technologies. There's always a balance between excitement and disillusionment. The Gartner hype cycle, as much as we don't like to think about it, applies to almost every single technology.

Speaker 2

17:33

Of course the magnitude of the peaks and the draws is different.

Speaker 3

17:37

But I would say we're at the peak

Speaker 2

17:40

of an inflated expectation with deep learning. And that's something we have to think about as we talk about some of the ideas and exciting possibilities of the future. And we're still driving cars that we'll talk about in future lectures in this course, we're at the same.

Speaker 2

17:55

In fact, we're a little bit beyond the peak. And so it's up to us, this is MIT and the engineers and the people working on this in the world to carry us through the draw, to carry us through the future as the ups and downs of the excitement progresses forward into the plateau of productivity. Why else not deep learning?

Speaker 3

18:22

If we look at real world applications,

Speaker 2

18:26

especially with humanoid robotics, robotic manipulation, and even, yes, autonomous vehicles, majority of the aspects of autonomous vehicles do not involve to an extensive amount machine learning to date. The problems are not formulated as data-driven learning. Instead, they're model-based optimization methods that don't learn from data over time.

Speaker 2

18:51

And then from the speakers these couple of weeks, we'll get to see how much machine learning is starting to creep in. But the example shown here with the Boston, with amazing humanoid robotics and Boston dynamics. To date almost no machine learning has been used except for trivial perception. The same with autonomous vehicles almost no machine learning and deep learning has been used except with perception.

Speaker 2

19:18

Some aspect of enhanced perception from the visual texture information. Plus what's becoming, what's starting to be used a little bit more is use of recurring neural networks to predict the future, to predict the intent of the different players in the scene in order to anticipate what the future is. But these are very early steps. Most of the success that you see today, the 10 million miles that Waymo has achieved, has been attributed mostly to non-machine learning methods.

Speaker 3

19:56

Why else not deep learning? Here's a really clean example of unintended consequences. Ethical issues we have to really think about.

Speaker 3

20:11

When an algorithm learns from data based on an objective function, a loss function, The power, the consequences of an algorithm that optimizes that function is not always obvious. Here's an example of a human player playing the game of Coast Runners with a it's a boat racing game where the task is to go around the racetrack and try to win the race. And the objective is to get as many points as possible. There are 3 ways to get points.

Speaker 3

20:44

The finishing time, how long it took you to finish, the finishing position, where you were in the ranking, and picking up quote unquote turbos, those little green things along the way, they give you points. Okay, simple enough. So we design an agent, in this case an RL agent, that optimizes for the rewards. And what we find on the right here, the optimal, the agent discovers that the optimals actually has nothing to do with finishing the race or the ranking.

Speaker 3

21:16

They can get much more points by just focusing on the turbos and collecting those little green dots because they regenerate. So you go in circles over and over and over, slamming into the wall, collecting the green turbos. Now that's a very clear example of a well-reasoned, a formulated objective function that has totally unexpected consequences, at least without sort of considering those consequences ahead of time. And so that shows the need for AI safety for a human in the loop of machine learning.

Speaker 3

21:55

That's why not deep learning exclusively. The challenge of deep learning algorithms, of deep learning applied, is to ask the right question and understand what the answers mean. You have to take a step back and look at the difference, the distinction, the levels, degrees of what the algorithm is accomplishing. For example, image classification is not necessarily scene understanding.

Speaker 3

22:30

In fact, it's very far from scene understanding. Classification may be very far from understanding. And the data sets can vary drastically across the different benchmarks and the data sets used. The professionally done photographs versus synthetically generated images versus real world data.

Speaker 3

22:52

And the real world data is where the big impact is. So oftentimes the 1 doesn't transfer to the other. That's the challenge of deep learning. Solving all of these problems of different lighting variations, of pose variation, interclass variation, all the things that we take for granted as human beings with our incredible perception system all have to be solved in order to gain greater and greater understanding of a scene.

Speaker 3

23:16

And all the other things we have to close the gap on that we're not even close to yet. Here's an image from the Andrej Karpathy blog from a few years ago of former President Obama stepping on a scale. We can classify, we can do semantic segmentation of the scene, we can do object detection, we can do a little bit of 3D reconstruction from a video version of the scene. But what we can't do well is all the things we take for granted.

Speaker 3

23:43

We can't tell the images in the mirrors versus in reality as different. We can't deal with the sparsity of information. Just

Speaker 2

23:53

a few pixels on President Obama's face, we can still identify him as the president. The 3D structure of the scene, that there's a foot on top of a scale, that there's human beings behind from a single image. Things we can trivially do using all the common sense semantic knowledge that we have cannot do.

Speaker 2

24:14

The physics of the scene, that there's gravity,

Speaker 3

24:18

And the biggest thing, the hardest thing, is

Speaker 2

24:20

what's on people's minds. And what's on people's minds about what's on other people's minds, and so on. Mental models of the world, being able to infer what people are thinking about.

Speaker 2

24:32

Being able to infer, there's been a lot of exciting work here at MIT about what people are looking at. But we're not even close to solving that problem either. But what they're thinking about, we're not even, we haven't even begun to really think about that problem. And we do it trivially as human beings.

Speaker 3

24:50

And I think at the core of that I think I'm harboring on the visual perception problem because it's 1 we take really for granted as human beings especially when trying to solve real-world problems especially when trying to solve autonomous driving, is we have 540 million years of data for visual perception, so we take it for granted. We don't realize how difficult it is and we kind of focus all our attention on this recent development of a hundred thousand years of abstract thought being able to play chess being able to reason but the visual perception is nevertheless extremely difficult at all at every single layer of what's required to perceive interpret and understand the fundamentals of a scene. And a trivial way to show that is just all the ways you can mess with these image classification systems by adding a little bit of noise.

Speaker 3

25:41

The last few years, there's been a lot of papers, a lot of work to show that you can mess with these systems by adding noise here with 99% accuracy, predict a dog, add a little bit of distortion, immediately the system predicts with 99% accuracy that it's an ostrich. And you can do that kind of manipulation with just a single pixel. So that's just a clean way to show the gap between image classification on an artificial data set like ImageNet and real world perception that has to be solved, especially for life critical situations like autonomous driving. I really like this Max Tegmark's visualization of this rising sea of the landscape of human competence from Hans Marwack.

Speaker 3

26:36

And this is the difference as we progress forward and we discuss some of these machine learning methods is there is the human intelligence, the general human intelligence, let's call Einstein here, that's able to generalize over all kinds of problems, over all kinds of, from the common sense to the incredibly complex.

Speaker 2

27:01

And then there is the way we've been doing, especially data-driven machine learning, which is savants, which is specialized intelligence, extremely smart at a particular task, but not being able to transfer except in the very narrow neighborhood on this little landscape of different of art, cinematography, book writing at the peaks and chess arithmetic and theorem proving and vision at the bottom in the lake. And there's this rising sea as we solve problem after problem, the question can the methodology and the approach of deep learning of everything we're doing now keep the sea rising? Or do fundamental breakthroughs have to happen in order to generalize and solve these problems?

Speaker 3

27:47

And so from the specialized where the successes are, the systems are essentially boiled down to given the data set and given the ground truth for that data set, here's the apartment cost in the Boston area, be able to input several parameters, and based on those parameters, predict the apartment cost. That's the basic premise approach behind the successful supervised deep learning systems today. If you have good enough data, there's good enough ground truth and can be formalized, we can solve it.

Speaker 3

28:26

Some of the recent promise that we will do an entire series of lectures in the third week on deeper enforcement learning, showed that from raw sensory information with very little annotation, through self play, where their systems learn without human supervision, are able to perform extremely well in these constrained contexts. The question of a video game. Here, pong to pixels, being able to perceive the raw pixels of this pong game as raw input and learn the fundamental quote-unquote physics of this game. Understand how it is this game behaves and how to be able to win this game.

Speaker 3

29:12

That's kind of a step toward general purpose artificial intelligence. But it is a very small step because it's in a simulated, very trivial situation. That's the challenge that's before us. Would less and less human supervision be able to solve huge real world problems from the top supervised learning where majority of the teaching is done by human beings throughout the annotation process through labeling all the data by showing different examples and further and further down to semi-supervised learning, reinforcement learning and supervised learning, removing the teacher from the picture and making that teacher extremely efficient when it is needed.

Speaker 3

29:59

Of course, data augmentation is 1 way, as we'll talk about, so taking a small number of examples and messing with that set of examples, augmenting that set of examples through trivial and through complex methods of cropping, stretching, shifting, and so on, including through generative networks, modifying those images to grow a small data set into a large 1 to minimize, to decrease further and further the input that's the human, the input of the human teacher. But still, that's quite far away from the incredibly efficient, both teaching and learning that humans do. This is a video, and there's many of them online for the first time, a human baby walking.

Speaker 2

30:54

We learned to do this,

Speaker 3

30:57

it's 1 shot learning. 1 day you're on all fours, and the next day you put your 2 hands up and then you figure out the rest. 1 shot.

Speaker 3

31:10

Well, you can kind of, ish, you can kind of play around with it. But the point is you're extremely efficient. With only

Speaker 2

31:19

a few examples are able to learn the fundamental aspect of how to solve a particular problem. Machines in most cases need thousands, millions and sometimes more examples depending on the life critical nature of the application.

Speaker 3

31:38

The data flow of Supervised learning systems is there's input data, there's a learning system, and there is output. Now in the training stage for the output, we have the ground truth. And so we use that ground truth to teach the system.

Speaker 3

32:02

In the testing stage, when it goes out into the wild, there's new input data over which we have to generalize with the learning system and have to make our best guess. In the training stage, the processes with neural networks is given the input data for which we have the ground truth, pass it through the model, get the prediction, and given that we have the ground truth, we can compare the prediction to the ground truth, look at the error, and based on the error, adjust the weights. The types of predictions we can make is regression and classification. Regression is a continuous and classification is categorical.

Speaker 3

32:37

Here, if we look at weather, the regression problem says what is the temperature going to be tomorrow and the classification formulation of that problem says is it going to be hot or cold or some threshold definition of what hot or cold is. That's regression classification. On the classification front it could be multi-class which is the standard formulation where you're tasked with saying what is, there's only, a particular entity can only be 1 thing, and then there's multi-label where a particular entity can be multiple things. And overall, the input to the system can be not just a single sample of the particular data set and the output doesn't have to be a particular sample of the ground truth data set.

Speaker 3

33:26

It can be a sequence, sequence to sequence, a single sample to a sequence, a sequence to a sample, and so on. From video captioning, where it's video captioning to translation, to natural language generation, to of course the one-to-one general computer vision. Okay, that's the bigger picture. Let's step back from the big to the small, to a single neuron, inspired by our own brain, the biological neural networks in our brain, and the computational block that is behind a lot of the intelligence in our mind.

Speaker 3

34:05

The artificial neuron has inputs with weights on them, plus a bias, an activation function and an output. It's inspired by this thing. As I showed it before, here visualizes the thalamocortical system with 3 million neurons and 476 million synapses. The full brain has a hundred billion, billion neurons and a thousand trillion synapses.

Speaker 3

34:34

ResNet and some of the other state-of-the-art networks have in tens, hundreds of millions of edges of synapses. The human brain has 10 million times more synapses than artificial neural networks and there's other differences. The topology is asynchronous and not constructed in layers. The learning algorithm for artificial neural networks is back propagation for our biological networks, we don't know.

Speaker 3

35:12

That's 1 of the mysteries of the human brain. There's ideas, but we really don't know. The power consumption, human brains are much more efficient than neural networks. That's 1 of the problems that we're trying to solve and ASICs are starting to begin to solve some of these problems.

Speaker 3

35:28

And the stages of learning. In the biological neural networks, you really never stop learning. You're always learning, always changing both on the hardware and the software. In artificial neural networks oftentimes there's a training stage, there's a distinct training stage and there's a distinct testing stage when you release the thing in the wild.

Speaker 3

35:47

Online learning is an exceptionally difficult thing that we're still in the very early stages of. This neuron takes a few inputs, the fundamental computational block behind neural networks. Takes a few inputs, applies weights, which are the parameters that are learned, sums them up, puts it into a nonlinear activation function after adding the bias, also a learned parameter, and gives an output. And the task of this neuron is to get excited based on certain aspects of the layers, features, inputs that followed before.

Speaker 3

36:26

And in that ability to discriminate, get excited by certain things and get not excited by other things, hold a little piece of information of whatever level of abstraction it is. So when you combine many of them together, you have knowledge. Different levels of abstractions form a knowledge base that's able to represent, understand or even act on a particular set of raw inputs. And you stack these neurons together in layers, both in width and depth, increasing further on and there's a lot of different architectural variants but they begin at this basic fact that with just a single hidden layer of a neural network the possibilities are endless.

Speaker 3

37:13

It can approximate in any arbitrary function. Adding a neural network with a single hidden layer can approximate any function. That means any other neural network with multiple layers and so on is just interesting optimizations of how we can discover those functions. The possibilities are endless.

Speaker 3

37:36

And the other aspect here is the mathematical underpinnings of neural networks with the weights and the differentiable activation functions are such that in a few steps from the inputs to the outputs are deeply parallelizable. And that's why the other aspect on the compute, the parallelizability of neural networks is what enables some of the exciting advancements on the graphical processing unit, the GPUs and with ASICs, TPUs. The ability to run across machines, across GPU units, in a very large distributed scale to be able to train and perform inference on neural networks. Activation functions.

Speaker 3

38:31

These activation functions put together are tasked with optimizing a loss function. For regression, that loss function is mean squared error, usually, there's a lot of variance. And for classification, it's cross-entropy loss. In the cross-entropy loss the ground truth is 0,1.

Speaker 3

38:51

In the mean squared error it's

Speaker 2

38:56

real numbered.

Speaker 3

39:00

And so with the loss function and the weights and the bias and the activation functions propagating forward through the network from the input to the output. Using the loss function we use the algorithm of Bragg propagation. I wish I did an entire lecture last time.

Speaker 3

39:18

To adjust the weights. To have the air flow backwards to the network and adjust the weights such that once again the weights that were responsible for producing the correct output are increased and the weights that were responsible for producing the incorrect output were decreased. The forward pass gives you the error. The backward pass computes the gradients and based on the gradients the optimization algorithm combined with a learning rate adjust the weights.

Speaker 3

39:57

The learning rate is how fast the network learns and all of this is possible on the numerical computation side with automatic differentiation. The optimization problem, given those gradients that are computed and the backward flow to the network of the gradients, is stochastic gradient descent. There's a lot of variance of this optimization algorithms that solve various problems, from dying Rayleigh's to vanishing gradients. There's a lot of different parameters on momentum and so on that's really just boiled down to all the different problems that are solved with nonlinear optimization.

Speaker 3

40:37

Mini-batch size, what is the right size of a batch or really it's called mini-batch when it's not the entire data set to you based on which to compute the gradients to adjust the learning. Do you do it over a very large amount or do you do it with stochastic gradient descent for every single sample of the data? If you listen to Yann LeCun in a lot of recent literature is small mini batch sizes are good. He says training with large mini batches is bad for your health.

Speaker 3

41:11

More importantly it's bad for your test error. Friends don't let friends use mini batches larger than 32. Larger batch size means more computational speed because you don't have to update the weights as often. But smaller batch size empirically produces better generalization.

Speaker 3

41:34

The problem we're often on the broader scale of learning trying to solve is overfitting. And the way we solve it is through regularization. We want to train on a data set without memorizing to an extent that you only do well in that trained data set. So you want it to be generalizable into future into the future things that you haven't seen yet.

Speaker 3

42:04

So obviously this is a problem for small data sets and also for sets of parameters that you choose. Here shown an example of a sine curve trying to fit a particular data versus a ninth degree polynomial trying to fit a particular set of data with the blue dots. The ninth degree polynomial is overfitting. It does very well for that particular set of samples but does not generalize well in the general case.

Speaker 3

42:32

And the trade-off here is as you train further and further at a certain point there's a deviation between the error being decreased to 0 on the training set and going to 1 on the test set. And that's the balance we have to strike. That's done with the validation set. So you take a piece of the training set for which you have the ground truth and you call it the validation set and you set it aside and you evaluate the performance of your system on that validation set.

Speaker 3

43:09

And after you notice that your trained network is performing poorly on the validation set for a prolonged period of time that's when you stop. That's early stoppage. Basically it's getting better and better and better and then there is some period of time there's always noise of course and after some period of time it's definitely getting worse. That's when you need to stop there.

Speaker 3

43:31

So that provides an automated way to discovering when you need to stop. And there's a lot of other regularization methodologies. Of course, as I mentioned, dropout is very interesting approach for and its variance of simply with a certain kind of probability randomly remove nodes in the network. Both incoming and outgoing edges.

Speaker 3

43:56

Randomly throughout the training process. And There's normalization. Normalization is obviously always applied at the input. So whenever you have a data set as different lighting conditions, different variations, they get different sources and so on, you have to all kind of put it on the same level ground.

Speaker 3

44:21

So that we're learning the fundamental aspects of the input data as opposed to the some less relevant semantic information like lighting variation and so on. So we usually always normalize for example if it's computer vision with pixels from 0 to 255, you always normalize to 0 to 1, or negative 1 to 1, or normalize based on the mean and the standard deviation. That's something you should almost always do. The thing that enabled a lot of breakthrough performances in the past few years is batch normalization.

Speaker 3

44:58

It's performing this kind of same normalization later on in the network. Looking at the inputs to the hidden layers and normalizing based on the batch of data which you're training normalized based on the mean and the standard deviation. As batch normalization with batch renormalization fixes a few of the challenges which is given that you're normalizing during the training on the mini batches in the training data set that doesn't directly map to the inference stage and the testing. And so it allows, by keeping a running average, across both training and testing, you're able to asymptotically approach a global normalization.

Speaker 3

45:47

So there's this idea across all the weights, not just the inputs, across all the weights to normalize the world in all the levels of abstractions that you're forming. And Batch Renorm solves a lot of these problems during inference. And there's a lot of other ideas from layer to weight to instance normalization to group normalization. And you can play with a lot of these ideas in the TensorFlow playground on playground.tensorflow.org that I highly recommend.

Speaker 3

46:15

So now let's run through a bunch of different ideas. Some of which we'll cover in future lectures. Of what is all of this in this world of deep learning. From computer vision to deep reinforcement learning to the different small level techniques to the large natural language processing.

Speaker 3

46:33

So convolutional neural networks, the thing that enables image classification. So these convolutional filters slide over the image and are able to take advantage of the spatial invariance of visual information that a cat in the top left corner is the same as features associated with cats in the top right corner and so on. Images are just a set of numbers and our task is to take that image and produce a classification and use the spatial variance of visual information to make that, to slide a convolution filter across the image and learn that filter as opposed to assigning equal value to features that are present in various regions of the image. And stacked on top of each other, these convolution filters can form high-level abstractions of visual information and images.

Speaker 3

47:28

With AlexNet, as I've mentioned, and the ImageNet dataset and challenge, captivating the world of what is possible with neural networks have been further and further improved, superseding human performance with a special note, GoogleNet with the Inception module. There's different ideas that came along, ResNet with the residual blocks, and SCNet most recently. So the object detection problem is a step, The next step in the visual recognition. So the image classification is just taking the entire image and saying what's in the image.

Speaker 3

48:07

Object detection localization is saying, find all the objects of interest in the scene and classify them. The region based methods like shown here, FastRCNN takes the image, uses convolution neural network to extract features in that image and generate region proposals. Here's a bunch of candidates that you should look at. And within those candidates, it classifies what they are and generates 4 parameters, the bounding box that's that thing, that captures that thing.

Speaker 3

48:38

So object detection localization ultimately boils down to a bounding box, a rectangle with a class that's the most likely class that's in that bounding box. And you can really summarize region-based methods as you generate the region proposal here a little pseudocode and do a for loop over the region proposals and perform detection on that for loop. The single-shot methods remove the for loop. There's a single pass through, you add a bunch of, take a, for example, here shown SSD, take a pre-trained neural network that's been trained to do image classification, stack a bunch of convolutional layers on top, from each layer extract features that are then able to generate in a single pass classes, bounding boxes, bounding box predictions and the class associated with those bounding box.

Speaker 3

49:36

The trade-off here, and this is where the popular YOLO V1, 2, 3 come from. The trade-off here oftentimes is in performance and accuracy. So single shot methods are often less performant, especially in terms of accuracy on objects that are really far away, or rather objects that are small in the image or really large. Then the next step up in visual perception, visual understanding is semantic segmentation.

Speaker 3

50:10

That's where the tutorial that we presented here on GitHub is covering. Semantic segmentation is the task of now as opposed to a bounding box or classifying the entire image or detecting the object as a bounding box is assigning at a pixel level the boundaries of what the object is. Every single in full scene segmentation, classifying what every single pixel, which class that pixel belongs to. And the fundamental aspect there, so we'll cover a little bit, or a lot more, on Wednesday is taking a image classification network, chopping it off at some point, and then having, which is performing the encoding step of compressing a representation of the scene and taking that representation with a decoder, up sampling in a dense way, So taking that representation and up sampling the pixel level classification.

Speaker 3

51:12

So that up sampling, there's a lot of tricks that we'll talk through that are interesting but ultimately it boils down to the encoding step of forming a representation of what's going on in the scene, and then the decoding step that upsamples the pixel level annotation classification of all the individual pixels. And as I mentioned here, the underlying idea applied most extensively, most successfully in computer vision is transfer learning. Most commonly applied way of transfer learning is taking a pre-trained neural network like ResNet and chopping it off at some point, chopping off the fully connected layer, layers, some parts of the layers, and then taking a dataset, a new dataset, and retraining that network. So what is this useful for?

Speaker 3

52:06

For every single application computer vision in industry, when you have a specific application, like you want to build a pedestrian detector. If you want to build a pedestrian detector and you have a pedestrian dataset, it's useful to take ResNet trained on ImageNet or COCO trained in the general case of vision perception and taking that network, chopping off some of the layers, and then retraining on your specialized pedestrian dataset. And depending on how large that data set is, some of the previous layers from the pre-trained network should be fixed, frozen, and sometimes not, depending on how large the data is. And this is extremely effective in computer vision, but also in audio speech and NLP.