title

MIT Introduction to Deep Learning | 6.S191

description

MIT Introduction to Deep Learning 6.S191: Lecture 1
*New 2023 Edition*
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: http://introtodeeplearning.com/
Lecture Outline
0:00 - Introduction
8:14 - Course information
11:33 - Why deep learning?
14:48 - The perceptron
20:06 - Perceptron example
23:14 - From perceptrons to neural networks
29:34 - Applying neural networks
32:29 - Loss functions
35:12 - Training and gradient descent
40:25 - Backpropagation
44:05 - Setting the learning rate
48:09 - Batched gradient descent
51:25 - Regularization: dropout and early stopping
57:16 - Summary
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detail

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44.376, 'text': "So this week of Intro to Deep Learning, we're going to cover a ton of material in just one week.", 'start': 38.833, 'duration': 5.543}, {'end': 51.139, 'text': "You'll learn the foundations of this really, really fascinating and exciting field of deep learning and artificial intelligence.", 'start': 44.756, 'duration': 6.383}, {'end': 60.534, 'text': "And more importantly, you're going to get hands-on experience actually reinforcing what you learn in the lectures as part of hands-on software labs.", 'start': 52.111, 'duration': 8.423}, {'end': 68.157, 'text': 'Now, over the past decade, AI and deep learning have really had a huge resurgence and had incredible successes.', 'start': 61.735, 'duration': 6.422}, {'end': 74.62, 'text': 'And a lot of problems that even just a decade ago we thought were not really even solvable in the near future.', 'start': 68.657, 'duration': 5.963}, {'end': 77.541, 'text': "now we're solving with deep learning, with incredible ease.", 'start': 74.62, 'duration': 2.921}, {'end': 84.129, 'text': 'Now, this past year in particular of 2022 has been an incredible year for deep learning progress.', 'start': 78.368, 'duration': 5.761}, {'end': 90.151, 'text': "And I'd like to say that actually, this past year in particular has been the year of generative deep learning,", 'start': 84.67, 'duration': 5.481}, {'end': 97.753, 'text': 'using deep learning to generate brand new types of data that have never been seen before and never existed in reality.', 'start': 90.151, 'duration': 7.602}], 'summary': 'Intro to deep learning covers a lot in one week, with hands-on labs. deep learning has seen incredible progress in 2022, especially in generative deep learning.', 'duration': 65.923, 'max_score': 31.83, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s31830.jpg'}, {'end': 151.711, 'src': 'embed', 'start': 121.214, 'weight': 5, 'content': [{'end': 132.437, 'text': 'Hi, everybody, and welcome to MIT Fit Pass 191, the official introductory course on deep learning here at MIT.', 'start': 121.214, 'duration': 11.223}, {'end': 142.682, 'text': "We've learned is revolutionizing so many fields, from robotics to medicine and everything in between.", 'start': 134.113, 'duration': 8.569}, {'end': 151.711, 'text': "You'll learn the fundamentals of this field and how you can build some of these incredible algorithms.", 'start': 143.863, 'duration': 7.848}], 'summary': 'Mit fit pass 191: introductory course on deep learning, revolutionizing robotics and medicine.', 'duration': 30.497, 'max_score': 121.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s121214.jpg'}, {'end': 271.377, 'src': 'embed', 'start': 231.611, 'weight': 0, 'content': [{'end': 240.916, 'text': 'but generate full synthetic environments where we can train autonomous vehicles entirely in simulation and deploy them on full-scale vehicles in the real world seamlessly.', 'start': 231.611, 'duration': 9.305}, {'end': 247.267, 'text': 'The videos here you see are actually from a data-driven simulator from neural networks generated, called Vista,', 'start': 241.396, 'duration': 5.871}, {'end': 251.389, 'text': 'that we actually built here at MIT and have open sourced to the public.', 'start': 247.267, 'duration': 4.122}, {'end': 255.771, 'text': 'So all of you can actually train and build the future of autonomy and self-driving cars.', 'start': 251.409, 'duration': 4.362}, {'end': 258.411, 'text': 'And of course, it goes far beyond this as well.', 'start': 256.411, 'duration': 2}, {'end': 266.836, 'text': 'Deep learning can be used to generate content directly from how we speak and the language that we convey to it from prompts that we say.', 'start': 258.471, 'duration': 8.365}, {'end': 271.377, 'text': 'Deep learning can reason about the prompts in natural language in English,', 'start': 267.614, 'duration': 3.763}], 'summary': 'Mit has open sourced vista, a data-driven simulator for training autonomous vehicles, and highlights the broad applications of deep learning in generating content from natural language prompts.', 'duration': 39.766, 'max_score': 231.611, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s231611.jpg'}], 'start': 9.364, 'title': 'Deep learning advancements', 'summary': 'Covers mit intro to deep learning, offering hands-on experience and discussing recent progress in generative deep learning. it also explores the impact of deep learning, highlighting its growth in creating synthetic videos and autonomous vehicle training environments using neural networks.', 'chapters': [{'end': 97.753, 'start': 9.364, 'title': 'Mit intro to deep learning', 'summary': 'Introduces mit intro to deep learning, covering a fast-paced program with hands-on experience, recent progress in deep learning, and the emergence of generative deep learning in 2022.', 'duration': 88.389, 'highlights': ['The MIT Intro to Deep Learning program offers hands-on experience and covers the foundations of deep learning and artificial intelligence in just one week, emphasizing reinforcement through hands-on software labs.', 'The past year of 2022 has been highlighted as the year of generative deep learning, using deep learning to generate new types of data that have never existed before.', 'AI and deep learning have experienced a huge resurgence in the past decade, solving problems that were previously considered unsolvable with incredible ease.']}, {'end': 311.163, 'start': 97.813, 'title': 'Deep learning revolution', 'summary': 'Discusses the impact of deep learning, showcasing its growth over the years, from creating synthetic videos to generating autonomous vehicle training environments using neural networks.', 'duration': 213.35, 'highlights': ['Deep learning can now generate full synthetic environments for training autonomous vehicles and 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software that can generate software as well.', 'start': 311.163, 'duration': 3.461}, {'end': 317.785, 'text': 'We can also have algorithms that can take language prompts.', 'start': 314.844, 'duration': 2.941}, {'end': 319.526, 'text': 'For example, a prompt like this.', 'start': 318.186, 'duration': 1.34}, {'end': 322.427, 'text': 'Write code in TensorFlow to train a neural network.', 'start': 320.006, 'duration': 2.421}, {'end': 328.032, 'text': 'And not only will it write the code and create that neural network,', 'start': 324.048, 'duration': 3.984}, {'end': 337.522, 'text': "but it will have the ability to reason about the code that it's generated and walk you through step by step explaining the process and procedure all the way from the ground up to you,", 'start': 328.032, 'duration': 9.49}, {'end': 340.705, 'text': 'so that you can actually learn how to do this process as well.', 'start': 337.522, 'duration': 3.183}], 'summary': 'Software generating algorithms can write code and explain it step by step, enabling learning.', 'duration': 29.542, 'max_score': 311.163, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s311163.jpg'}, {'end': 378.305, 'src': 'embed', 'start': 342.904, 'weight': 1, 'content': [{'end': 350.349, 'text': 'I think some of these examples really just highlight how far deep learning and these methods have come in the past six years since we started this course.', 'start': 342.904, 'duration': 7.445}, {'end': 354.532, 'text': 'And you saw that example just a few years ago from that introductory video.', 'start': 350.389, 'duration': 4.143}, {'end': 356.573, 'text': "But now we're seeing such incredible advances.", 'start': 354.612, 'duration': 1.961}, {'end': 364.338, 'text': "And the most amazing part of this course, in my opinion, is actually that within this one week, we're going to take you through, from the ground up,", 'start': 356.633, 'duration': 7.705}, {'end': 372.544, 'text': 'starting from today, all of the foundational building blocks that will allow you to understand and make all of this amazing advances possible.', 'start': 364.338, 'duration': 8.206}, {'end': 378.305, 'text': "So with that, hopefully now you're all super excited about what this class will teach.", 'start': 373.86, 'duration': 4.445}], 'summary': 'Deep learning has seen incredible advances in the past six years, and the course will cover foundational building blocks for understanding.', 'duration': 35.401, 'max_score': 342.904, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s342904.jpg'}, {'end': 445, 'src': 'embed', 'start': 421.673, 'weight': 4, 'content': [{'end': 431.456, 'text': 'Now, machine learning is simply a subset of AI which focuses specifically on how we can build a machine or teach a machine how to do this,', 'start': 421.673, 'duration': 9.783}, {'end': 434.237, 'text': 'from some experiences or data, for example.', 'start': 431.456, 'duration': 2.781}, {'end': 445, 'text': 'Now, deep learning goes one step beyond this and is a subset of machine learning which focuses explicitly on what are called neural networks and how we can build neural networks that can extract features in the data.', 'start': 434.917, 'duration': 10.083}], 'summary': 'Machine learning is a subset of ai that teaches machines using data, while deep learning focuses on neural networks to extract data features.', 'duration': 23.327, 'max_score': 421.673, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s421673.jpg'}, {'end': 501.529, 'src': 'embed', 'start': 472.304, 'weight': 5, 'content': [{'end': 477.968, 'text': "and we'll provide a very solid foundation for you, both on the technical side, through the lectures,", 'start': 472.304, 'duration': 5.664}, {'end': 483.652, 'text': 'which will happen in two parts throughout the class the first lecture and the second lecture, each one about one hour long,', 'start': 477.968, 'duration': 5.684}, {'end': 487.114, 'text': 'followed by a software lab which will immediately follow the lectures,', 'start': 483.652, 'duration': 3.462}, {'end': 495.86, 'text': 'which will try to reinforce a lot of what we cover in the technical part of the class and give you hands-on experience implementing those ideas.', 'start': 487.114, 'duration': 8.746}, {'end': 501.529, 'text': 'So this program is split between these two pieces, the technical lectures and the software labs.', 'start': 496.604, 'duration': 4.925}], 'summary': 'The program includes two one-hour lectures and software labs to reinforce learning.', 'duration': 29.225, 'max_score': 472.304, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s472304.jpg'}, {'end': 566.949, 'src': 'embed', 'start': 523.323, 'weight': 0, 'content': [{'end': 531.126, 'text': 'And of course, we have many awesome prizes that go with all of the software labs and the project competition at the end of the course.', 'start': 523.323, 'duration': 7.803}, {'end': 533.267, 'text': 'So maybe quickly to go through these.', 'start': 531.367, 'duration': 1.9}, {'end': 537.109, 'text': "Each day, like I said, we'll have dedicated software labs that couple with the lectures.", 'start': 533.387, 'duration': 3.722}, {'end': 543.891, 'text': "Starting today with Lab 1, you'll actually build a neural network keeping with this theme of generative AI.", 'start': 538.388, 'duration': 5.503}, {'end': 551.475, 'text': "You'll build a neural network that can learn, listen to a lot of music and actually learn how to generate brand new songs in that genre of music.", 'start': 543.931, 'duration': 7.544}, {'end': 561.006, 'text': "At the end, at the next level of the class on Friday, we'll host a project pitch competition where either you, individually or as part of a group,", 'start': 552.921, 'duration': 8.085}, {'end': 566.949, 'text': 'can participate and present an idea, a novel deep learning idea, to all of us.', 'start': 561.006, 'duration': 5.943}], 'summary': 'Participants will have dedicated software labs and a project competition, with the opportunity to build a neural network to generate new songs and pitch deep learning ideas.', 'duration': 43.626, 'max_score': 523.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s523323.jpg'}, {'end': 702.492, 'src': 'embed', 'start': 670.825, 'weight': 6, 'content': [{'end': 674.827, 'text': 'Myself and Ava will be your two main lecturers for the first part of the class.', 'start': 670.825, 'duration': 4.002}, {'end': 681.67, 'text': "We'll also be hearing, like I said in the later part of the class, from some guest lecturers who will share some really cutting-edge,", 'start': 675.507, 'duration': 6.163}, {'end': 683.411, 'text': 'state-of-the-art developments in deep learning.', 'start': 681.67, 'duration': 1.741}, {'end': 688.693, 'text': 'And, of course, I want to give a huge shout-out and thanks to all of our sponsors who, without their support,', 'start': 684.011, 'duration': 4.682}, {'end': 692.595, 'text': "this program wouldn't have been possible for yet again another year.", 'start': 688.693, 'duration': 3.902}, {'end': 693.375, 'text': 'So thank you all.', 'start': 692.775, 'duration': 0.6}, {'end': 702.492, 'text': "OK, so now with that, let's really dive into the really fun stuff of today's lecture, which is, you know, the technical part.", 'start': 695.109, 'duration': 7.383}], 'summary': 'Two main lecturers, guest lecturers, and sponsors make deep learning class possible.', 'duration': 31.667, 'max_score': 670.825, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s670825.jpg'}], 'start': 311.163, 'title': 'Deep learning advancements', 'summary': 'Delves into recent advancements in deep learning, such as software generation, language understanding algorithms, and rapid foundational block teaching. it also explores a comprehensive program for teaching deep learning, encompassing technical lectures, software labs, and a project competition with substantial prizes.', 'chapters': [{'end': 378.305, 'start': 311.163, 'title': 'Advances in deep learning', 'summary': 'Discusses the advancements in deep learning, including the capability to generate software, algorithms that can understand language prompts, and the promise of teaching foundational building blocks within a week.', 'duration': 67.142, 'highlights': ['Deep learning has evolved significantly in the past six years, showcasing the ability to generate software and understand language prompts.', 'The algorithms can write code in TensorFlow to train neural networks and provide step-by-step explanations, enabling users to learn the process.', 'The course promises to cover foundational building blocks in just one week, allowing individuals to comprehend and contribute to these advancements.']}, {'end': 713.076, 'start': 378.465, 'title': 'Understanding ai and deep learning', 'summary': 'Discusses the definitions of artificial intelligence, machine learning, and deep learning, and introduces a program focused on teaching deep learning, including technical lectures, software labs, and a project competition with significant prizes.', 'duration': 334.611, 'highlights': ['The program focuses on teaching deep learning through technical lectures and software labs, followed by a project competition with significant prizes, including an NVIDIA GPU for the first prize and a grand prize for developing robust and trustworthy AI models (Relevance: 5)', 'Artificial intelligence involves building algorithms that process information to inform future decisions, while machine learning teaches machines to do this from experiences or data, and deep learning goes further to focus on neural networks and extracting features from data (Relevance: 4)', 'The technical part of the class includes lectures happening in two parts, each about one hour long, followed by software labs reinforcing the covered concepts and providing hands-on experience (Relevance: 3)', 'The later part of the class will feature guest lecturers sharing cutting-edge developments in deep learning, and the program also acknowledges and thanks sponsors for their support (Relevance: 2)', 'The project pitch competition allows individuals or groups to present novel deep learning ideas, with prizes including significant hardware for building and training deep learning projects (Relevance: 1)']}], 'duration': 401.913, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s311163.jpg', 'highlights': ['The program focuses on teaching deep learning through technical lectures and software labs, followed by a project competition with significant prizes, including an NVIDIA GPU for the first prize and a grand prize for developing robust and trustworthy AI models', 'Deep learning has evolved significantly in the past six years, showcasing the ability to generate software and understand language prompts', 'The algorithms can write code in TensorFlow to train neural networks and provide step-by-step explanations, enabling users to learn the process', 'The course promises to cover foundational building blocks in just one week, allowing individuals to comprehend and contribute to these advancements', 'Artificial intelligence involves building algorithms that process information to inform future decisions, while machine learning teaches machines to do this from experiences or data, and deep learning goes further to focus on neural networks and extracting features from data', 'The technical part of the class includes lectures happening in two parts, each about one hour long, followed by software labs reinforcing the covered concepts and providing hands-on experience', 'The later part of the class will feature guest lecturers sharing cutting-edge developments in deep learning, and the program also acknowledges and thanks sponsors for their support', 'The project pitch competition allows individuals or groups to present novel deep learning ideas, with prizes including significant hardware for building and training deep learning projects']}, {'end': 1386.637, 'segs': [{'end': 871.886, 'src': 'embed', 'start': 830.605, 'weight': 0, 'content': [{'end': 836.45, 'text': 'Number one is that data is so much more pervasive than it has ever been before in our lifetimes.', 'start': 830.605, 'duration': 5.845}, {'end': 842.792, 'text': "These models are hungry for more data And we're living in the age of big data.", 'start': 836.99, 'duration': 5.802}, {'end': 847.134, 'text': 'More data is available to these models than ever before, and they thrive off of that.', 'start': 843.333, 'duration': 3.801}, {'end': 851.536, 'text': 'Secondly, these algorithms are massively parallelizable.', 'start': 847.695, 'duration': 3.841}, {'end': 853.237, 'text': 'They require a lot of compute,', 'start': 851.616, 'duration': 1.621}, {'end': 863.062, 'text': "and we're also at a unique time in history where we have the ability to train these extremely large scale algorithms and techniques that have existed for a very long time,", 'start': 853.237, 'duration': 9.825}, {'end': 866.083, 'text': 'but we can now train them due to the hardware advances that have been made.', 'start': 863.062, 'duration': 3.021}, {'end': 871.886, 'text': 'finally, due to open source toolboxes and software platforms like tensorflow, for example,', 'start': 866.743, 'duration': 5.143}], 'summary': 'Data is more pervasive, algorithms are massively parallelizable, and open source toolboxes enable training large-scale algorithms.', 'duration': 41.281, 'max_score': 830.605, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s830605.jpg'}, {'end': 943.886, 'src': 'embed', 'start': 914.265, 'weight': 4, 'content': [{'end': 918.708, 'text': 'So I want to make sure that everyone in the audience understands exactly what a perceptron is and how it works.', 'start': 914.265, 'duration': 4.443}, {'end': 925.212, 'text': "So let's start by first defining a perceptron as taking as input a set of inputs right?", 'start': 919.829, 'duration': 5.383}, {'end': 931.897, 'text': 'So on the left-hand side, you can see this perceptron takes m different inputs, 1 to m right?', 'start': 925.312, 'duration': 6.585}, {'end': 932.977, 'text': 'These are the blue circles.', 'start': 931.937, 'duration': 1.04}, {'end': 935.059, 'text': "We're denoting these inputs as x's.", 'start': 933.458, 'duration': 1.601}, {'end': 943.886, 'text': 'Each of these numbers, each of these inputs, is then multiplied by a corresponding weight, which we can call W right?', 'start': 936.72, 'duration': 7.166}], 'summary': 'Explaining the concept of a perceptron, taking m inputs and multiplying by corresponding weights.', 'duration': 29.621, 'max_score': 914.265, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s914265.jpg'}, {'end': 1211.75, 'src': 'embed', 'start': 1187.717, 'weight': 3, 'content': [{'end': 1196.526, 'text': "And in fact, if you introduce nonlinear activation functions to your solution, that's exactly what allows you to deal with these types of problems.", 'start': 1187.717, 'duration': 8.809}, {'end': 1200.57, 'text': 'Nonlinear activation functions allow you to deal with nonlinear types of data.', 'start': 1196.826, 'duration': 3.744}, {'end': 1205.735, 'text': "And that's what exactly makes neural networks so powerful at their core.", 'start': 1201.851, 'duration': 3.884}, {'end': 1211.75, 'text': "So let's understand this maybe with a very simple example, walking through this diagram of a perceptron one more time.", 'start': 1206.806, 'duration': 4.944}], 'summary': 'Nonlinear activation functions make neural networks powerful at dealing with nonlinear data.', 'duration': 24.033, 'max_score': 1187.717, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1187717.jpg'}, {'end': 1332.429, 'src': 'heatmap', 'start': 1284.774, 'weight': 0.716, 'content': [{'end': 1293.12, 'text': 'And if we consider the space of all of the possible inputs that this neural network could see, we can actually plot this on a decision boundary.', 'start': 1284.774, 'duration': 8.346}, {'end': 1301.687, 'text': 'We can plot this two-dimensional line as a decision boundary, as a plane separating these two components of our space.', 'start': 1293.16, 'duration': 8.527}, {'end': 1308.858, 'text': "In fact, not only is it a single plane, there's a directionality component, depending on which side of the plane that we live on.", 'start': 1302.594, 'duration': 6.264}, {'end': 1312.18, 'text': 'If we see an input, for example here negative one,', 'start': 1309.298, 'duration': 2.882}, {'end': 1318.263, 'text': 'two we actually know that it lives on one side of the plane and it will have a certain type of output.', 'start': 1312.18, 'duration': 6.083}, {'end': 1321.745, 'text': 'In this case, that output is going to be positive right?', 'start': 1318.283, 'duration': 3.462}, {'end': 1325.867, 'text': 'Because in this case, when we plug those components into our equation,', 'start': 1321.805, 'duration': 4.062}, {'end': 1332.429, 'text': "we'll get a positive number that passes through the nonlinear component and that gets propagated through as well.", 'start': 1325.867, 'duration': 6.562}], 'summary': 'Neural network can be visualized with a decision boundary separating inputs, yielding specific outputs.', 'duration': 47.655, 'max_score': 1284.774, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1284774.jpg'}, {'end': 1361.201, 'src': 'embed', 'start': 1332.91, 'weight': 5, 'content': [{'end': 1337.612, 'text': "Of course, if you're on the other side of the space, you're going to have the opposite result right?", 'start': 1332.91, 'duration': 4.702}, {'end': 1342.054, 'text': 'And that thresholding function is going to essentially live at this decision boundary.', 'start': 1337.712, 'duration': 4.342}, {'end': 1347.237, 'text': 'So, depending on which side of the space you live on, that thresholding function, that sigmoid function,', 'start': 1342.094, 'duration': 5.143}, {'end': 1350.719, 'text': 'is going to then control how you move to one side or the other.', 'start': 1347.237, 'duration': 3.482}, {'end': 1361.201, 'text': 'Now, in this particular example, this is very convenient because we can actually visualize and I can draw this exact full space for you on this slide.', 'start': 1352.677, 'duration': 8.524}], 'summary': 'Thresholding function at decision boundary controls movement in space.', 'duration': 28.291, 'max_score': 1332.91, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1332910.jpg'}], 'start': 713.156, 'title': 'Deep learning history and perceptrons', 'summary': 'Delves into the history and advancements of deep learning, highlighting key advances and the current enthusiasm for studying it. it also provides a comprehensive understanding of perceptrons in neural networks, including their role, inputs, weights, bias, activation functions, and the formation of decision boundaries through a simple example.', 'chapters': [{'end': 892.035, 'start': 713.156, 'title': 'Deep learning: history and advancements', 'summary': 'Discusses the history of machine learning, the shift to deep learning, and the current enthusiasm for studying it, citing key advances such as the pervasiveness of data, the parallelizability of algorithms, and the accessibility of open source toolboxes.', 'duration': 178.879, 'highlights': ['Data is more pervasive than ever, leading to hungry models thriving off more available data.', 'Algorithms are massively parallelizable, taking advantage of hardware advances to train large-scale algorithms.', 'Advances in open source toolboxes and software platforms like tensorflow have made training and building neural networks easier.']}, {'end': 1386.637, 'start': 892.035, 'title': 'Understanding perceptrons in neural networks', 'summary': 'Explains the concept of perceptrons, their role in neural networks, including the inputs, weights, bias, activation function, and the importance of nonlinear activation functions for dealing with nonlinear data, demonstrated through a simple example, and how the decision boundary is formed.', 'duration': 494.602, 'highlights': ['The role of perceptrons in neural networks, including the inputs, weights, bias, and activation function, is explained, and the forward propagation of information through a perceptron is defined.', 'The importance of nonlinear activation functions for dealing with nonlinear data is emphasized, along with a simple example demonstrating its significance in solving complex problems.', "The formation of the decision boundary and its role in separating data points is illustrated, emphasizing the thresholding function's control over classifying inputs based on the side of the decision boundary."]}], 'duration': 673.481, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s713156.jpg', 'highlights': ['Algorithms are massively parallelizable, taking advantage of hardware advances to train large-scale algorithms.', 'Advances in open source toolboxes and software platforms like tensorflow have made training and building neural networks easier.', 'Data is more pervasive than ever, leading to hungry models thriving off more available data.', 'The importance of nonlinear activation functions for dealing with nonlinear data is emphasized, along with a simple example demonstrating its significance in solving complex problems.', 'The role of perceptrons in neural networks, including the inputs, weights, bias, and activation function, is explained, and the forward propagation of information through a perceptron is defined.', "The formation of the decision boundary and its role in separating data points is illustrated, emphasizing the thresholding function's control over classifying inputs based on the side of the decision boundary."]}, {'end': 2395.436, 'segs': [{'end': 1433.111, 'src': 'embed', 'start': 1405.174, 'weight': 0, 'content': [{'end': 1408.417, 'text': "So let's revisit again this previous diagram of the perceptron.", 'start': 1405.174, 'duration': 3.243}, {'end': 1409.878, 'text': 'If again,', 'start': 1409.097, 'duration': 0.781}, {'end': 1420.567, 'text': 'just to reiterate one more time this core piece of information that I want all of you to take away from this class is how a perceptron works and how it propagates information to its decision.', 'start': 1409.878, 'duration': 10.689}, {'end': 1421.748, 'text': 'There are three steps.', 'start': 1420.967, 'duration': 0.781}, {'end': 1423.069, 'text': 'First is the dot product.', 'start': 1421.848, 'duration': 1.221}, {'end': 1424.61, 'text': 'Second is the bias.', 'start': 1423.669, 'duration': 0.941}, {'end': 1426.372, 'text': 'And third is the nonlinearity.', 'start': 1425.09, 'duration': 1.282}, {'end': 1430.375, 'text': 'And you keep repeating this process for every single perceptron in your neural network.', 'start': 1426.852, 'duration': 3.523}, {'end': 1433.111, 'text': "Let's simplify the diagram a little bit.", 'start': 1431.59, 'duration': 1.521}], 'summary': 'Understanding perceptron: dot product, bias, nonlinearity in neural networks.', 'duration': 27.937, 'max_score': 1405.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1405174.jpg'}, {'end': 1521.001, 'src': 'embed', 'start': 1492.64, 'weight': 1, 'content': [{'end': 1495.262, 'text': 'we just have now two neurons, two perceptrons.', 'start': 1492.64, 'duration': 2.622}, {'end': 1499.085, 'text': 'each perceptron will control the output for its associated piece.', 'start': 1495.262, 'duration': 3.823}, {'end': 1501.476, 'text': 'right, and So now we have two outputs.', 'start': 1499.085, 'duration': 2.391}, {'end': 1502.977, 'text': 'each one is a normal perceptron.', 'start': 1501.476, 'duration': 1.501}, {'end': 1504.377, 'text': 'it takes all of the inputs.', 'start': 1502.977, 'duration': 1.4}, {'end': 1506.698, 'text': 'so they both take the same inputs, but amazingly.', 'start': 1504.377, 'duration': 2.321}, {'end': 1513.599, 'text': 'Now, with this mathematical understanding, we can start to build our first neural network entirely from scratch.', 'start': 1507.238, 'duration': 6.361}, {'end': 1515.06, 'text': 'So what does that look like?', 'start': 1513.599, 'duration': 1.461}, {'end': 1518.74, 'text': 'so we can start by firstly initializing these two components.', 'start': 1515.06, 'duration': 3.68}, {'end': 1521.001, 'text': 'the first component that we saw was the weight matrix.', 'start': 1518.74, 'duration': 2.261}], 'summary': 'Two perceptrons controlling two outputs with the same inputs.', 'duration': 28.361, 'max_score': 1492.64, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1492640.jpg'}, {'end': 1554.436, 'src': 'embed', 'start': 1534.7, 'weight': 2, 'content': [{'end': 1545.049, 'text': "So the only remaining step now, after we've defined these parameters of our layer, is to now define how this forward propagation of information works.", 'start': 1534.7, 'duration': 10.349}, {'end': 1549.152, 'text': "And that's exactly those three main components that I've been stressing to you.", 'start': 1545.069, 'duration': 4.083}, {'end': 1554.436, 'text': 'So we can create this call function to do exactly that, to define this forward propagation of information.', 'start': 1549.272, 'duration': 5.164}], 'summary': 'Define forward propagation using call function with three main components.', 'duration': 19.736, 'max_score': 1534.7, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1534700.jpg'}, {'end': 1634.504, 'src': 'heatmap', 'start': 1465.03, 'weight': 3, 'content': [{'end': 1469.291, 'text': 'that piece is going to be applied to that activation function.', 'start': 1465.03, 'duration': 4.261}, {'end': 1474.913, 'text': 'now the final output here is simply going to be G, which is our activation function.', 'start': 1469.291, 'duration': 5.622}, {'end': 1478.031, 'text': 'Z, right, Z is going to be.', 'start': 1476.109, 'duration': 1.922}, {'end': 1480.112, 'text': 'basically, you can think of the state of this neuron.', 'start': 1478.031, 'duration': 2.081}, {'end': 1484.195, 'text': "it's the result of that dot product plus bias.", 'start': 1480.112, 'duration': 4.083}, {'end': 1491.28, 'text': 'now, if we want to define and build up a multi-layered output neural network, if we want two outputs to this function, for example,', 'start': 1484.195, 'duration': 7.085}, {'end': 1492.64, 'text': "it's a very simple procedure.", 'start': 1491.28, 'duration': 1.36}, {'end': 1495.262, 'text': 'we just have now two neurons, two perceptrons.', 'start': 1492.64, 'duration': 2.622}, {'end': 1499.085, 'text': 'each perceptron will control the output for its associated piece.', 'start': 1495.262, 'duration': 3.823}, {'end': 1501.476, 'text': 'right, and So now we have two outputs.', 'start': 1499.085, 'duration': 2.391}, {'end': 1502.977, 'text': 'each one is a normal perceptron.', 'start': 1501.476, 'duration': 1.501}, {'end': 1504.377, 'text': 'it takes all of the inputs.', 'start': 1502.977, 'duration': 1.4}, {'end': 1506.698, 'text': 'so they both take the same inputs, but amazingly.', 'start': 1504.377, 'duration': 2.321}, {'end': 1513.599, 'text': 'Now, with this mathematical understanding, we can start to build our first neural network entirely from scratch.', 'start': 1507.238, 'duration': 6.361}, {'end': 1515.06, 'text': 'So what does that look like?', 'start': 1513.599, 'duration': 1.461}, {'end': 1518.74, 'text': 'so we can start by firstly initializing these two components.', 'start': 1515.06, 'duration': 3.68}, {'end': 1521.001, 'text': 'the first component that we saw was the weight matrix.', 'start': 1518.74, 'duration': 2.261}, {'end': 1524.982, 'text': "Excuse me, the weight vector it's a vector of weights in this case.", 'start': 1521.801, 'duration': 3.181}, {'end': 1533.719, 'text': "And the second component is the bias vector that we're going to multiply with the dot product of all of our inputs by our weights.", 'start': 1526.593, 'duration': 7.126}, {'end': 1545.049, 'text': "So the only remaining step now, after we've defined these parameters of our layer, is to now define how this forward propagation of information works.", 'start': 1534.7, 'duration': 10.349}, {'end': 1549.152, 'text': "And that's exactly those three main components that I've been stressing to you.", 'start': 1545.069, 'duration': 4.083}, {'end': 1554.436, 'text': 'So we can create this call function to do exactly that, to define this forward propagation of information.', 'start': 1549.272, 'duration': 5.164}, {'end': 1558.253, 'text': "And the story here is exactly the same as we've been seeing it right?", 'start': 1555.325, 'duration': 2.928}, {'end': 1567.525, 'text': 'Matrix. multiply our inputs with our weights, Add a bias and then apply a nonlinearity and return the result.', 'start': 1558.473, 'duration': 9.052}, {'end': 1576.149, 'text': 'And that literally, this code will run, this will define a full neural network layer that you can then take like this.', 'start': 1568.165, 'duration': 7.984}, {'end': 1581.231, 'text': "And, of course, actually luckily for all of you, all of that code, which wasn't much code,", 'start': 1577.309, 'duration': 3.922}, {'end': 1584.032, 'text': "that's been abstracted away by these libraries like TensorFlow.", 'start': 1581.231, 'duration': 2.801}, {'end': 1589.754, 'text': 'you can simply call functions like this, which will actually replicate exactly that piece of code.', 'start': 1584.032, 'duration': 5.722}, {'end': 1594.975, 'text': "So you don't need to necessarily copy all of that code down, you can just call it.", 'start': 1590.714, 'duration': 4.261}, {'end': 1602.037, 'text': 'And with that understanding, we just saw how you could build a single layer, but of course,', 'start': 1596.515, 'duration': 5.522}, {'end': 1606.678, 'text': 'now you can actually start to think about how you can stack these layers as well.', 'start': 1602.037, 'duration': 4.641}, {'end': 1613.34, 'text': 'So, since we now have this transformation essentially from our inputs to a hidden output,', 'start': 1607.198, 'duration': 6.142}, {'end': 1624.562, 'text': 'you can think of this as basically how we can define some way of transforming those inputs into some new dimensional space,', 'start': 1613.34, 'duration': 11.222}, {'end': 1627.062, 'text': 'perhaps closer to the value that we want to predict.', 'start': 1624.562, 'duration': 2.5}, {'end': 1634.504, 'text': "And that transformation is going to be eventually learned to know how to transform those inputs into our desired outputs, and we'll get to that later.", 'start': 1627.242, 'duration': 7.262}], 'summary': 'Building a neural network layer from scratch, with 2 outputs and mathematical understanding.', 'duration': 169.474, 'max_score': 1465.03, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1465030.jpg'}, {'end': 1728.364, 'src': 'embed', 'start': 1699.721, 'weight': 4, 'content': [{'end': 1705.966, 'text': 'Now, if you want to stack these types of solutions on top of each other, these layers on top of each other,', 'start': 1699.721, 'duration': 6.245}, {'end': 1710.73, 'text': 'you can not only define one layer very easily, but you can actually create what are called sequential models.', 'start': 1705.966, 'duration': 4.764}, {'end': 1712.171, 'text': 'These sequential models.', 'start': 1710.85, 'duration': 1.321}, {'end': 1719.497, 'text': 'you can define one layer after another and they define basically the forward propagation of information, not just from the neuron level,', 'start': 1712.171, 'duration': 7.326}, {'end': 1720.698, 'text': 'but now from the layer level.', 'start': 1719.497, 'duration': 1.201}, {'end': 1728.364, 'text': 'Every layer will be fully connected to the next layer, and the inputs of the secondary layer will be all of the outputs of the prior layer.', 'start': 1720.858, 'duration': 7.506}], 'summary': 'Sequential models allow stacking layers for forward propagation of information, creating fully connected layers.', 'duration': 28.643, 'max_score': 1699.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1699721.jpg'}, {'end': 1998.754, 'src': 'embed', 'start': 1966.602, 'weight': 6, 'content': [{'end': 1968.744, 'text': 'can it do better minimize that mistake??', 'start': 1966.602, 'duration': 2.142}, {'end': 1973.668, 'text': 'So in neural network language, those mistakes are called losses.', 'start': 1969.625, 'duration': 4.043}, {'end': 1982.374, 'text': "And specifically, you want to define what's called a loss function, which is going to take as input your prediction and the true prediction.", 'start': 1974.568, 'duration': 7.806}, {'end': 1988.278, 'text': 'And how far away your prediction is from the true prediction tells you how big of a loss there is.', 'start': 1982.914, 'duration': 5.364}, {'end': 1998.754, 'text': "So, for example, Let's say we want to build a neural network to do classification of or sorry, actually, even before that,", 'start': 1989.278, 'duration': 9.476}], 'summary': 'In neural network, defining a loss function measures prediction accuracy.', 'duration': 32.152, 'max_score': 1966.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1966602.jpg'}, {'end': 2143.953, 'src': 'heatmap', 'start': 2021.196, 'weight': 5, 'content': [{'end': 2028.217, 'text': 'And what we really ultimately want to do is over the course of an entire data set, not just one data point of mistakes.', 'start': 2021.196, 'duration': 7.021}, {'end': 2029.918, 'text': 'we want to say over the entire data set.', 'start': 2028.217, 'duration': 1.701}, {'end': 2035.142, 'text': 'We want to minimize all of the mistakes on average that this neural network makes.', 'start': 2030.578, 'duration': 4.564}, {'end': 2043.348, 'text': "So if we look at the problem, like I said, of binary classification, will I pass this class or will I not? There's a yes or a no answer.", 'start': 2036.863, 'duration': 6.485}, {'end': 2044.749, 'text': 'That means binary classification.', 'start': 2043.388, 'duration': 1.361}, {'end': 2050.841, 'text': "Now, we can use what's called a loss function of the softmax cross entropy loss.", 'start': 2045.819, 'duration': 5.022}, {'end': 2064.907, 'text': "And for those of you who aren't familiar, this notion of cross entropy is actually developed here at MIT by Claude Shannon, who is a visionary.", 'start': 2050.981, 'duration': 13.926}, {'end': 2068.007, 'text': 'He did his masters here over 50 years ago.', 'start': 2065.087, 'duration': 2.92}, {'end': 2072.268, 'text': 'He introduced this notion of cross entropy and that was, you know,', 'start': 2068.068, 'duration': 4.2}, {'end': 2077.77, 'text': 'pivotal in the ability for us to train these types of neural networks even now into the future.', 'start': 2072.268, 'duration': 5.502}, {'end': 2089.192, 'text': "So let's start by, instead of predicting a binary cross-entropy output, what if we wanted to predict a final grade of your class score, for example?", 'start': 2078.65, 'duration': 10.542}, {'end': 2091.632, 'text': "That's no longer a binary output, yes or no?", 'start': 2089.232, 'duration': 2.4}, {'end': 2093.493, 'text': "it's actually a continuous variable, right?", 'start': 2091.632, 'duration': 1.861}, {'end': 2095.913, 'text': "It's the grade, let's say, out of 100 points.", 'start': 2093.513, 'duration': 2.4}, {'end': 2099.234, 'text': 'what is the value of your score in the class project?', 'start': 2095.913, 'duration': 3.321}, {'end': 2103.016, 'text': "right?. For this type of loss, we can use what's called a mean squared error loss.", 'start': 2099.234, 'duration': 3.782}, {'end': 2110.162, 'text': 'You can think of this literally as just subtracting your predicted grade from the true grade and minimizing that distance apart.', 'start': 2103.036, 'duration': 7.126}, {'end': 2125.433, 'text': "So I think now we're ready to really put all of this information together and tackle this problem of training a neural network right to not just identify how erroneous it is,", 'start': 2112.564, 'duration': 12.869}, {'end': 2132.098, 'text': 'how large its loss is, but, more importantly, minimize that loss as a function of seeing all of this training data that it observes.', 'start': 2125.433, 'duration': 6.665}, {'end': 2137.226, 'text': 'So we know that we want to find this neural network, like we mentioned before,', 'start': 2133.823, 'duration': 3.403}, {'end': 2143.953, 'text': 'that minimizes this empirical risk or this empirical loss averaged across our entire data set.', 'start': 2137.226, 'duration': 6.727}], 'summary': 'Minimize mistakes in neural network across entire data set using loss functions.', 'duration': 78.866, 'max_score': 2021.196, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2021196.jpg'}], 'start': 1386.677, 'title': 'Neural network fundamentals', 'summary': 'Covers building neural networks from scratch, neural network basics, and training techniques, including gradient descent. it explains the process, structure, training process, and practical examples of predicting class performance based on lecture attendance and project hours.', 'chapters': [{'end': 1680.801, 'start': 1386.677, 'title': 'Building neural networks from scratch', 'summary': 'Explains the process of building a neural network from scratch, starting with the basic concept of a perceptron, then expanding to a multi-layered neural network, and finally, the forward propagation of information is defined for each layer.', 'duration': 294.124, 'highlights': ['The process of building a neural network starts with understanding the basic concept of a perceptron, which involves three key steps: dot product, bias, and nonlinearity.', 'In a multi-layered neural network, each perceptron controls the output for its associated piece, and with mathematical understanding, a neural network can be built entirely from scratch.', 'Defining the forward propagation of information involves matrix multiplication of inputs and weights, adding a bias, and applying a nonlinearity, which can be encapsulated in a call function.', 'The transformation from inputs to a hidden output in a neural network allows for the definition of a way to transform inputs into a new dimensional space, closer to the desired outputs.']}, {'end': 2035.142, 'start': 1681.667, 'title': 'Neural network basics', 'summary': 'Covers the basic concepts of building a neural network, including the structure of sequential models and the process of training the network using loss functions, with a practical example of predicting class performance based on lecture attendance and project hours.', 'duration': 353.475, 'highlights': ['The chapter explains the concept of sequential models, where layers are stacked on top of each other, with each layer fully connected to the next, leading to the creation of deep neural networks.', 'The transcript provides a practical example of using a neural network to predict class performance based on lecture attendance and project hours, highlighting the need to train the network to minimize mistakes using loss functions.', 'The concept of loss functions is detailed, emphasizing their role in training the neural network by quantifying the mistakes between predicted and true values to minimize overall errors.']}, {'end': 2395.436, 'start': 2036.863, 'title': 'Neural network training & gradient descent', 'summary': 'Discusses the use of loss functions in binary and continuous classification, the development of cross entropy by claude shannon, the concept of empirical risk and the process of gradient descent for training neural networks.', 'duration': 358.573, 'highlights': ['The concept of cross entropy was developed at MIT by Claude Shannon, enabling the training of neural networks.', 'The transition from binary to continuous classification involves using the mean squared error loss function.', 'The algorithm of gradient descent is used to minimize the empirical loss averaged across the entire data set during neural network training.']}], 'duration': 1008.759, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s1386677.jpg', 'highlights': ['The process of building a neural network starts with understanding the basic concept of a perceptron, involving dot product, bias, and nonlinearity.', 'In a multi-layered neural network, each perceptron controls the output for its associated piece, and with mathematical understanding, a neural network can be built entirely from scratch.', 'Defining the forward propagation of information involves matrix multiplication of inputs and weights, adding a bias, and applying a nonlinearity, encapsulated in a call function.', 'The transformation from inputs to a hidden output in a neural network allows for the definition of a way to transform inputs into a new dimensional space, closer to the desired outputs.', 'The chapter explains the concept of sequential models, where layers are stacked on top of each other, with each layer fully connected to the next, leading to the creation of deep neural networks.', 'The transcript provides a practical example of using a neural network to predict class performance based on lecture attendance and project hours, highlighting the need to train the network to minimize mistakes using loss functions.', 'The concept of loss functions is detailed, emphasizing their role in training the neural network by quantifying the mistakes between predicted and true values to minimize overall errors.', 'The concept of cross entropy was developed at MIT by Claude Shannon, enabling the training of neural networks.', 'The transition from binary to continuous classification involves using the mean squared error loss function.', 'The algorithm of gradient descent is used to minimize the empirical loss averaged across the entire data set during neural network training.']}, {'end': 2850.656, 'segs': [{'end': 2519.442, 'src': 'heatmap', 'start': 2407.222, 'weight': 0.942, 'content': [{'end': 2411.504, 'text': 'And that will tell the neural network if it needs to move the weights in a certain direction or not.', 'start': 2407.222, 'duration': 4.282}, {'end': 2415.252, 'text': 'But I never actually told you how to compute this.', 'start': 2412.631, 'duration': 2.621}, {'end': 2421.353, 'text': "And I think that's an extremely important part because if you don't know that, then you can't train your neural network.", 'start': 2415.592, 'duration': 5.761}, {'end': 2424.614, 'text': 'This is a critical part of training neural networks.', 'start': 2421.573, 'duration': 3.041}, {'end': 2430.115, 'text': 'And that process of computing this line, this gradient line, is known as backpropagation.', 'start': 2424.694, 'duration': 5.421}, {'end': 2434.896, 'text': "So let's do a very quick intro to backpropagation and how it works.", 'start': 2430.255, 'duration': 4.641}, {'end': 2439.536, 'text': "So again, let's start with the simplest neural network in existence.", 'start': 2436.294, 'duration': 3.242}, {'end': 2443.117, 'text': 'This neural network has one input, one output, and only one neuron.', 'start': 2439.636, 'duration': 3.481}, {'end': 2444.878, 'text': 'This is as simple as it gets.', 'start': 2443.657, 'duration': 1.221}, {'end': 2449.68, 'text': 'We want to compute the gradient of our loss with respect to our weight.', 'start': 2445.718, 'duration': 3.962}, {'end': 2453.162, 'text': "In this case, let's compute it with respect to W2, the second weight.", 'start': 2449.9, 'duration': 3.262}, {'end': 2461.105, 'text': 'So this derivative is going to tell us how much a small change in this weight will affect our loss.', 'start': 2454.195, 'duration': 6.91}, {'end': 2466.413, 'text': 'If a small change, if we change our weight a little bit in one direction, will it increase our loss or decrease our loss?', 'start': 2461.626, 'duration': 4.787}, {'end': 2469.863, 'text': 'So to compute that, we can write out this derivative', 'start': 2467.662, 'duration': 2.201}, {'end': 2476.225, 'text': 'We can start with applying the chain rule backwards from the loss function through the output.', 'start': 2469.943, 'duration': 6.282}, {'end': 2482.288, 'text': 'Specifically, what we can do is we can actually just decompose this derivative into two components.', 'start': 2476.706, 'duration': 5.582}, {'end': 2490.131, 'text': 'The first component is the derivative of our loss with respect to our output, multiplied by the derivative of our output with respect to w2, right?', 'start': 2482.388, 'duration': 7.743}, {'end': 2498.554, 'text': 'This is just a standard instantiation of the chain rule, with this original derivative that we had on the left-hand side.', 'start': 2490.171, 'duration': 8.383}, {'end': 2506.825, 'text': "Let's suppose we wanted to compute the gradients of the weight before that, which in this case are not W1, but W, excuse me, not W2, but W1.", 'start': 2499.916, 'duration': 6.909}, {'end': 2513.599, 'text': 'Well, all we do is replace W2 with W1, and that chain rule still holds right?', 'start': 2508.976, 'duration': 4.623}, {'end': 2519.442, 'text': 'That same equation holds, but now you can see on the red component, that last component of the chain rule,', 'start': 2513.639, 'duration': 5.803}], 'summary': 'Backpropagation computes gradient for training neural networks.', 'duration': 112.22, 'max_score': 2407.222, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2407222.jpg'}, {'end': 2443.117, 'src': 'embed', 'start': 2415.592, 'weight': 2, 'content': [{'end': 2421.353, 'text': "And I think that's an extremely important part because if you don't know that, then you can't train your neural network.", 'start': 2415.592, 'duration': 5.761}, {'end': 2424.614, 'text': 'This is a critical part of training neural networks.', 'start': 2421.573, 'duration': 3.041}, {'end': 2430.115, 'text': 'And that process of computing this line, this gradient line, is known as backpropagation.', 'start': 2424.694, 'duration': 5.421}, {'end': 2434.896, 'text': "So let's do a very quick intro to backpropagation and how it works.", 'start': 2430.255, 'duration': 4.641}, {'end': 2439.536, 'text': "So again, let's start with the simplest neural network in existence.", 'start': 2436.294, 'duration': 3.242}, {'end': 2443.117, 'text': 'This neural network has one input, one output, and only one neuron.', 'start': 2439.636, 'duration': 3.481}], 'summary': 'Backpropagation is crucial for training neural networks and involves computing the gradient line.', 'duration': 27.525, 'max_score': 2415.592, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2415592.jpg'}, {'end': 2565.512, 'src': 'embed', 'start': 2537.776, 'weight': 7, 'content': [{'end': 2541.518, 'text': "all the way back to the weight that we're interested in in this example right.", 'start': 2537.776, 'duration': 3.742}, {'end': 2544.04, 'text': 'so we first computed the derivative with respect to w two.', 'start': 2541.518, 'duration': 2.522}, {'end': 2548.063, 'text': 'Then we can back propagate that and use that information also with w one.', 'start': 2544.78, 'duration': 3.283}, {'end': 2553.066, 'text': "that's why we really call it back propagation, because this process occurs from the output all the way back to the input.", 'start': 2548.063, 'duration': 5.003}, {'end': 2559.85, 'text': 'Now we repeat this process essentially many, many times over the course of training,', 'start': 2554.827, 'duration': 5.023}, {'end': 2565.512, 'text': 'by propagating these gradients over and over again through the network, all the way from the output to the inputs,', 'start': 2559.85, 'duration': 5.662}], 'summary': 'Back propagation occurs from output to input during training.', 'duration': 27.736, 'max_score': 2537.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2537776.jpg'}, {'end': 2600.687, 'src': 'embed', 'start': 2575.197, 'weight': 0, 'content': [{'end': 2580.4, 'text': "and how we can use that to improve the loss ultimately, because that's our final goal in this class.", 'start': 2575.197, 'duration': 5.203}, {'end': 2584.537, 'text': "So that's the backpropagation algorithm.", 'start': 2582.816, 'duration': 1.721}, {'end': 2587.659, 'text': "That's the core of training neural networks.", 'start': 2584.777, 'duration': 2.882}, {'end': 2589.76, 'text': "In theory, it's very simple.", 'start': 2588.239, 'duration': 1.521}, {'end': 2593.403, 'text': "It's really just an instantiation of the chain rule.", 'start': 2589.8, 'duration': 3.603}, {'end': 2600.687, 'text': "But let's touch on some insights that make training neural networks actually extremely complicated in practice,", 'start': 2594.523, 'duration': 6.164}], 'summary': 'Backpropagation is key to improving loss in neural network training.', 'duration': 25.49, 'max_score': 2575.197, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2575197.jpg'}, {'end': 2650.382, 'src': 'embed', 'start': 2621.601, 'weight': 1, 'content': [{'end': 2627.465, 'text': 'This is an illustration from a paper that came out several years ago, where they tried to actually visualize the landscape of very,', 'start': 2621.601, 'duration': 5.864}, {'end': 2628.606, 'text': 'very deep neural networks.', 'start': 2627.465, 'duration': 1.141}, {'end': 2630.927, 'text': "And that's what this landscape actually looks like.", 'start': 2629.266, 'duration': 1.661}, {'end': 2633.389, 'text': "That's what you're trying to deal with and find the minimum in this space.", 'start': 2630.967, 'duration': 2.422}, {'end': 2635.891, 'text': 'And you can imagine the challenges that come with that.', 'start': 2633.549, 'duration': 2.342}, {'end': 2644.458, 'text': "So to cover the challenges, let's first think of and recall that update equation defined in gradient descent.", 'start': 2636.894, 'duration': 7.564}, {'end': 2650.382, 'text': "So I didn't talk too much about this parameter, eta, but now let's spend a bit of time thinking about this.", 'start': 2644.518, 'duration': 5.864}], 'summary': 'Visualizing landscape of deep neural networks, challenges in finding minimum, and focusing on update equation parameters.', 'duration': 28.781, 'max_score': 2621.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2621601.jpg'}, {'end': 2694.555, 'src': 'embed', 'start': 2653.124, 'weight': 4, 'content': [{'end': 2660.368, 'text': 'It determines basically how big of a step we need to take in the direction of our gradient in every single iteration of backpropagation.', 'start': 2653.124, 'duration': 7.244}, {'end': 2664.998, 'text': 'In practice, even setting the learning rate can be very challenging.', 'start': 2661.434, 'duration': 3.564}, {'end': 2669.442, 'text': 'You as the designer of the neural network have to set this value, this learning rate.', 'start': 2665.038, 'duration': 4.404}, {'end': 2672.846, 'text': 'And how do you pick this value, right? So that can actually be quite difficult.', 'start': 2669.963, 'duration': 2.883}, {'end': 2677.11, 'text': 'It has really large consequences when building a neural network.', 'start': 2672.886, 'duration': 4.224}, {'end': 2684.047, 'text': 'So for example, If we set the learning rate too low, then we learn very slowly.', 'start': 2677.13, 'duration': 6.917}, {'end': 2687.349, 'text': "So let's assume we start on the right-hand side here at that initial guess.", 'start': 2684.107, 'duration': 3.242}, {'end': 2694.555, 'text': "If our learning rate is not large enough, not only do we converge slowly, we actually don't even converge to the global minimum right?", 'start': 2687.509, 'duration': 7.046}], 'summary': 'Setting the learning rate is crucial in backpropagation for convergence and network building.', 'duration': 41.431, 'max_score': 2653.124, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2653124.jpg'}, {'end': 2830.819, 'src': 'embed', 'start': 2808.789, 'weight': 6, 'content': [{'end': 2821.993, 'text': 'There is a very thriving community in the deep learning research community that focuses on developing and designing new algorithms for learning rate adaptation and faster optimization of large neural networks like these.', 'start': 2808.789, 'duration': 13.204}, {'end': 2830.819, 'text': "And during your labs you'll actually get the opportunity to not only try out a lot of these different adaptive algorithms which you can see here,", 'start': 2822.933, 'duration': 7.886}], 'summary': 'Thriving deep learning community focuses on developing new algorithms for faster optimization of large neural networks.', 'duration': 22.03, 'max_score': 2808.789, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2808789.jpg'}], 'start': 2395.476, 'title': 'Backpropagation and setting learning rates', 'summary': 'Explains the backpropagation process for training neural networks and discusses challenges in setting learning rates, emphasizing the computation of gradients, optimizing neural networks, and the need for adaptive algorithms in the research community.', 'chapters': [{'end': 2434.896, 'start': 2395.476, 'title': 'Backpropagation and neural networks', 'summary': 'Explains the critical process of backpropagation, which is essential for training neural networks, by demonstrating how the loss changes as the weights move and introducing the concept of backpropagation as the computation of the gradient line.', 'duration': 39.42, 'highlights': ['The process of computing the gradient line, known as backpropagation, is crucial for training neural networks, as it determines whether the loss will increase or decrease as the weights move.', 'Understanding backpropagation is essential for training neural networks, as it is a critical part of the training process and enables the network to adjust the weights in the right direction.']}, {'end': 2635.891, 'start': 2436.294, 'title': 'Neural network backpropagation', 'summary': 'Explains the backpropagation algorithm, which involves computing gradients of weights by recursively applying the chain rule, and highlights the challenges of optimizing neural networks in practice, with insights on visualizing the landscape of deep neural networks.', 'duration': 199.597, 'highlights': ['The backpropagation algorithm involves computing gradients of weights by recursively applying the chain rule, to determine how much a small change in weights affects the loss function, ultimately aiming to improve the loss (relevance score: 5)', 'The visualization of the landscape of very deep neural networks poses challenges in finding the minimum in the space, making the optimization of neural networks extremely complicated in practice (relevance score: 4)', 'The backpropagation process occurs from the output all the way back to the input, involving propagating gradients through the network to determine the impact of weight changes on the loss function (relevance score: 3)']}, {'end': 2850.656, 'start': 2636.894, 'title': 'Challenges in setting learning rates', 'summary': 'Discusses the challenges in setting the learning rate for neural networks, the consequences of setting it too low or too high, and the need for intelligent adaptive algorithms, with a focus on the thriving research community in developing such algorithms.', 'duration': 213.762, 'highlights': ['The learning rate determines the size of the step in the direction of the gradient in each backpropagation iteration, posing challenges for setting it in neural network design.', 'Setting the learning rate too low results in slow convergence, potentially leading to getting stuck in local minima, while setting it too high can cause overshooting and divergence from the solution.', 'The development of adaptive learning rate algorithms is a thriving area in deep learning research, with a focus on faster optimization of large neural networks and the opportunity to uncover the benefits of different adaptive algorithms in practical labs.']}], 'duration': 455.18, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2395476.jpg', 'highlights': ['The backpropagation algorithm involves computing gradients of weights by recursively applying the chain rule, aiming to improve the loss (relevance score: 5)', 'The visualization of the landscape of very deep neural networks poses challenges in finding the minimum in the space, making the optimization of neural networks extremely complicated in practice (relevance score: 4)', 'The process of computing the gradient line, known as backpropagation, is crucial for training neural networks, determining whether the loss will increase or decrease as the weights move', 'Understanding backpropagation is essential for training neural networks, enabling the network to adjust the weights in the right direction', 'The learning rate determines the size of the step in the direction of the gradient in each backpropagation iteration, posing challenges for setting it in neural network design', 'Setting the learning rate too low results in slow convergence, potentially leading to getting stuck in local minima, while setting it too high can cause overshooting and divergence from the solution', 'The development of adaptive learning rate algorithms is a thriving area in deep learning research, focusing on faster optimization of large neural networks and the benefits of different adaptive algorithms in practical labs', 'The backpropagation process occurs from the output all the way back to the input, involving propagating gradients through the network to determine the impact of weight changes on the loss function']}, {'end': 3487.84, 'segs': [{'end': 3064.901, 'src': 'embed', 'start': 3020.667, 'weight': 0, 'content': [{'end': 3025.351, 'text': 'the stochasticity is significantly reduced and the accuracy of our gradient is much improved.', 'start': 3020.667, 'duration': 4.684}, {'end': 3033.998, 'text': "So normally we're thinking of batch sizes, mini batch sizes, roughly on the order of 100 data points, tens or hundreds of data points.", 'start': 3026.432, 'duration': 7.566}, {'end': 3041.164, 'text': 'This is much faster, obviously, to compute than gradient descent and much more accurate to compute compared to stochastic gradient descent,', 'start': 3034.138, 'duration': 7.026}, {'end': 3043.846, 'text': 'which is that single point example.', 'start': 3041.164, 'duration': 2.682}, {'end': 3056.195, 'text': 'So this increase in gradient accuracy allows us to essentially converge to our solution much quicker than it could have been possible in practice due to gradient descent limitations.', 'start': 3044.946, 'duration': 11.249}, {'end': 3063.02, 'text': 'It also means that we can increase our learning rate, because we can trust each of those gradients much more efficiently, right?', 'start': 3056.255, 'duration': 6.765}, {'end': 3064.901, 'text': "We're now averaging over a batch.", 'start': 3063.16, 'duration': 1.741}], 'summary': 'Reduced stochasticity, improved gradient accuracy, faster convergence, and increased learning rate due to batch averaging.', 'duration': 44.234, 'max_score': 3020.667, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s3020667.jpg'}, {'end': 3246.313, 'src': 'embed', 'start': 3222.332, 'weight': 3, 'content': [{'end': 3232.801, 'text': 'So regularization and having techniques for regularization has extreme implications towards the success of neural networks and having them generalized beyond training data far into our testing domain.', 'start': 3222.332, 'duration': 10.469}, {'end': 3241.525, 'text': 'The most popular technique for regularization in deep learning is called dropout, and the idea of dropout is actually very simple.', 'start': 3233.973, 'duration': 7.552}, {'end': 3246.313, 'text': "it's let's revisit it by drawing this picture of deep neural networks that we saw earlier in today's lecture.", 'start': 3241.525, 'duration': 4.788}], 'summary': 'Regularization techniques like dropout are crucial for the success of neural networks in generalizing beyond training data.', 'duration': 23.981, 'max_score': 3222.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s3222332.jpg'}, {'end': 3487.84, 'src': 'embed', 'start': 3462, 'weight': 4, 'content': [{'end': 3467.064, 'text': "And we've seen some tips and tricks for optimizing these systems end to end.", 'start': 3462, 'duration': 5.064}, {'end': 3480.577, 'text': "In the next lecture we'll hear from Ava on deep sequence modeling using RNNs and specifically this very exciting new type of model called the transformer architecture and attention mechanisms.", 'start': 3468.191, 'duration': 12.386}, {'end': 3487.84, 'text': "So maybe let's resume the class in about five minutes after we have a chance to swap speakers, and thank you so much for all of your attention.", 'start': 3481.257, 'duration': 6.583}], 'summary': 'Tips for optimizing systems. next: deep sequence modeling with rnns and transformer architecture. class resumes in 5 minutes.', 'duration': 25.84, 'max_score': 3462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s3462000.jpg'}], 'start': 2850.656, 'title': 'Neural network training and overfitting', 'summary': 'Discusses the benefits of batching data in neural network training, such as faster convergence and increased learning rates through mini-batches. it also explores the challenge of overfitting, emphasizing the importance of regularization and techniques like dropout and early stopping to improve generalization and prevent overfitting.', 'chapters': [{'end': 3086.075, 'start': 2850.656, 'title': 'Neural network training: batching data for faster optimization', 'summary': 'Discusses the concept of batching data in neural network training, emphasizing the computational advantages and improved accuracy achieved by using mini-batches, typically comprising tens or hundreds of data points, which enables faster convergence and increased learning rates, ultimately leading to significant speedups in computation, particularly when utilizing gpus.', 'duration': 235.419, 'highlights': ['Mini-batching data in neural network training involves using batches of tens or hundreds of data points, providing computational advantages and improved gradient accuracy, leading to faster convergence and increased learning rates.', 'The computational advantages of mini-batching data include significantly reduced stochasticity and increased accuracy of gradients compared to stochastic gradient descent and computational efficiency compared to gradient descent.', 'The increased gradient accuracy achieved through mini-batching allows for quicker convergence to solutions and the ability to increase learning rates, ultimately enabling faster learning and the possibility of massive parallelization using GPUs.']}, {'end': 3487.84, 'start': 3086.315, 'title': 'Neural network overfitting and regularization', 'summary': 'Discusses the challenge of overfitting in neural networks, the importance of regularization, and the techniques of dropout and early stopping to prevent overfitting and improve generalization. it also summarizes the fundamental building blocks of neural networks and hints at the upcoming lecture on deep sequence modeling using rnns and transformer architecture.', 'duration': 401.525, 'highlights': ['The challenge of overfitting in neural networks and the importance of regularization', 'Description of the regularization techniques of dropout and early stopping', 'Summary of fundamental building blocks of neural networks and a hint at the upcoming lecture on deep sequence modeling using RNNs and transformer architecture']}], 'duration': 637.184, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/QDX-1M5Nj7s/pics/QDX-1M5Nj7s2850656.jpg', 'highlights': ['Mini-batching data provides computational advantages and improved gradient accuracy, leading to faster convergence and increased learning rates.', 'Mini-batching reduces stochasticity and increases gradient accuracy compared to stochastic gradient descent, improving computational efficiency.', 'Increased gradient accuracy through mini-batching allows for quicker convergence and the ability to increase learning rates, enabling faster learning and massive parallelization using GPUs.', 'Overfitting in neural networks is a significant challenge, emphasizing the importance of regularization techniques like dropout and early stopping.', 'Fundamental building blocks of neural networks are summarized, hinting at the upcoming lecture on deep sequence modeling using RNNs and transformer architecture.']}], 'highlights': ['Deep learning can generate full synthetic environments for training autonomous vehicles and has accelerated at a faster rate than before, as seen at MIT in the development of Vista, a data-driven simulator.', 'The MIT Intro to Deep Learning program offers hands-on experience and covers the foundations of deep learning and artificial intelligence in just one week, emphasizing reinforcement through hands-on software labs.', 'The past year of 2022 has been highlighted as the year of generative deep learning, using deep learning to generate new types of data that have never existed before.', 'The program focuses on teaching deep learning through technical lectures and software labs, followed by a project competition with significant prizes, including an NVIDIA GPU for the first prize and a grand prize for developing robust and trustworthy AI models', 'Algorithms are massively parallelizable, taking advantage of hardware advances to train large-scale algorithms.', 'The backpropagation algorithm involves computing gradients of weights by recursively applying the chain rule, aiming to improve the loss (relevance score: 5)', 'Mini-batching data provides computational advantages and improved gradient accuracy, leading to faster convergence and increased learning rates.', 'Overfitting in neural networks is a significant challenge, emphasizing the importance of regularization techniques like dropout and early stopping.']}