title
MIT 6.S191 (2018): Introduction to Deep Learning

description
MIT Introduction to Deep Learning 6.S191: Lecture 1 Foundations of Deep Learning Lecturer: Alexander Amini January 2018 Lecture 1 - Introduction to Deep Learning: https://www.youtube.com/watch?v=JN6H4rQvwgY Lecture 2 - Deep Sequence Modeling: https://www.youtube.com/watch?v=CznICCPa63Q Lecture 3 - Deep Computer Vision: https://www.youtube.com/watch?v=NVH8EYPHi30 Lecture 4 - Deep Generative Models: https://www.youtube.com/watch?v=JVb54xhEw6Y Lecture 5 - Deep Reinforcement Learning: https://www.youtube.com/watch?v=s5qqjyGiBdc Lecture 6 - Limitations and New Frontiers: https://www.youtube.com/watch?v=l_yWLAQg7LU Lecture 7 - Issues in Image Classification (Google Guest): https://www.youtube.com/watch?v=QYwESy6isuc Lecture 8 - Faster ML Development with Tensorflow (Google Guest): https://www.youtube.com/watch?v=FkHWKq86tSw Lecture 9 - Deep Learning, A Personal Perspective (NVIDIA Guest): https://www.youtube.com/watch?v=Z7YMDwzUTds Lecture 10 - Beyond Deep Learning (IBM Guest): https://www.youtube.com/watch?v=mNqVGB2HkXg Lecture 11 - Computer Vision Meets Social Networks (Tencent Guest): https://www.youtube.com/watch?v=aFEnWHxUd7s For lectures, slides and lab materials: http://introtodeeplearning.com

detail
{'title': 'MIT 6.S191 (2018): Introduction to Deep Learning', 'heatmap': [], 'summary': 'Mit 6.s191 course on introduction to deep learning emphasizes building intelligent algorithms, surpassing human-level accuracy in imagenet challenge, neural network basics, activation functions, training networks for continuous valued functions, optimizing learning rates, and addressing overfitting in machine learning.', 'chapters': [{'end': 60.358, 'segs': [{'end': 60.358, 'src': 'embed', 'start': 2.523, 'weight': 0, 'content': [{'end': 3.224, 'text': 'Good morning, everyone.', 'start': 2.523, 'duration': 0.701}, {'end': 6.128, 'text': 'Thank you all for joining us.', 'start': 3.745, 'duration': 2.383}, {'end': 14.298, 'text': "This is MIT 6S191, and we'd like to welcome you to this course on Introduction to Deep Learning.", 'start': 6.588, 'duration': 7.71}, {'end': 20.287, 'text': "So in this course, you'll learn how to build remarkable algorithms,", 'start': 15.34, 'duration': 4.947}, {'end': 29.053, 'text': 'intelligent algorithms capable of solving very complex problems that just a decade ago were not even feasible to solve.', 'start': 20.287, 'duration': 8.766}, {'end': 33.337, 'text': "And let's just start with this notion of intelligence.", 'start': 30.054, 'duration': 3.283}, {'end': 44.986, 'text': 'So at a very high level, intelligence is the ability to process information so that it can be used to inform future predictions and decisions.', 'start': 34.618, 'duration': 10.368}, {'end': 57.275, 'text': "Now, when this intelligence is not engineered, but rather a biological inspiration, such as in humans, it's called human intelligence.", 'start': 46.004, 'duration': 11.271}, {'end': 60.358, 'text': "But when it's engineered, we refer to it as artificial intelligence.", 'start': 57.455, 'duration': 2.903}], 'summary': 'Mit 6s191 course on introduction to deep learning focuses on building intelligent algorithms capable of solving complex problems. intelligence is the ability to process information for future predictions and decisions.', 'duration': 57.835, 'max_score': 2.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY2523.jpg'}], 'start': 2.523, 'title': 'Intro to deep learning', 'summary': 'Introduces mit 6s191 course on introduction to deep learning, highlighting the ability to build intelligent algorithms, capable of solving complex problems and distinguishing between human and artificial intelligence.', 'chapters': [{'end': 60.358, 'start': 2.523, 'title': 'Intro to deep learning', 'summary': 'Introduces mit 6s191 course on introduction to deep learning, highlighting the ability to build intelligent algorithms capable of solving complex problems and distinguishing between human and artificial intelligence.', 'duration': 57.835, 'highlights': ['The chapter highlights the capability of building intelligent algorithms capable of solving very complex problems, which were not feasible to solve a decade ago.', 'It distinguishes between human intelligence and artificial intelligence, emphasizing the biological inspiration for human intelligence and the engineered nature of artificial intelligence.', 'The chapter defines intelligence as the ability to process information for informing future predictions and decisions, providing a high-level understanding of the concept.']}], 'duration': 57.835, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY2523.jpg', 'highlights': ['The chapter highlights the capability of building intelligent algorithms capable of solving very complex problems, which were not feasible to solve a decade ago.', 'It distinguishes between human intelligence and artificial intelligence, emphasizing the biological inspiration for human intelligence and the engineered nature of artificial intelligence.', 'The chapter defines intelligence as the ability to process information for informing future predictions and decisions, providing a high-level understanding of the concept.']}, {'end': 478.929, 'segs': [{'end': 136.453, 'src': 'embed', 'start': 104.471, 'weight': 0, 'content': [{'end': 108.252, 'text': 'And the winner in 2012, for the first time ever, was a deep learning-based system.', 'start': 104.471, 'duration': 3.781}, {'end': 116.736, 'text': 'And when it came out, it absolutely shattered all other competitors and crushed the competition and crushed the challenge.', 'start': 108.773, 'duration': 7.963}, {'end': 128.269, 'text': 'And today these deep learning based systems have actually surpassed human level accuracy on the ImageNet challenge and can actually recognize images even better than humans can.', 'start': 118.444, 'duration': 9.825}, {'end': 136.453, 'text': "Now in this class, you'll actually learn how to build complex vision systems, building a computer that knows how to see.", 'start': 130.71, 'duration': 5.743}], 'summary': 'In 2012, a deep learning system won, surpassing human accuracy in imagenet challenge.', 'duration': 31.982, 'max_score': 104.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY104471.jpg'}, {'end': 181.702, 'src': 'embed', 'start': 153.135, 'weight': 1, 'content': [{'end': 159.08, 'text': "You'll even make the network explain to you why it decided to diagnose, the way it diagnosed,", 'start': 153.135, 'duration': 5.945}, {'end': 163.144, 'text': 'by looking inside the network and understanding exactly why it made that decision.', 'start': 159.08, 'duration': 4.064}, {'end': 172.415, 'text': 'Deep neural networks can also be used to model sequences where your data points are not just single images, but rather temporally dependent.', 'start': 165.589, 'duration': 6.826}, {'end': 181.702, 'text': 'So for this, you can think of things like predicting the stock price, translating sentences from English to Spanish, or even generating new music.', 'start': 172.995, 'duration': 8.707}], 'summary': 'Deep neural networks can explain decisions and model sequences, e.g. stock price prediction, language translation, and music generation.', 'duration': 28.567, 'max_score': 153.135, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY153135.jpg'}, {'end': 217.007, 'src': 'embed', 'start': 191.098, 'weight': 5, 'content': [{'end': 199.641, 'text': 'learns the underlying representation of the notes that are being played in those songs and then learns to build brand new songs that have never been heard before.', 'start': 191.098, 'duration': 8.543}, {'end': 208.004, 'text': 'And there are really so many other incredible success stories of deep learning that I could talk for many hours about.', 'start': 202.422, 'duration': 5.582}, {'end': 210.885, 'text': "And we'll try to cover as many of these as possible as part of this course.", 'start': 208.044, 'duration': 2.841}, {'end': 217.007, 'text': "But I just wanted to give you an overview of some of the amazing ones that we'll be covering as part of the labs that you'll be implementing.", 'start': 211.525, 'duration': 5.482}], 'summary': 'Deep learning can create new songs and has many success stories.', 'duration': 25.909, 'max_score': 191.098, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY191098.jpg'}, {'end': 268.311, 'src': 'embed', 'start': 230.217, 'weight': 3, 'content': [{'end': 231.739, 'text': 'how they work and why they work.', 'start': 230.217, 'duration': 1.522}, {'end': 238.986, 'text': 'We will provide you with some of the practical skills to implement these algorithms and deploy them on your own machines.', 'start': 233.821, 'duration': 5.165}, {'end': 249.805, 'text': "And we'll talk to you about some of the state of art and cutting-edge research that's happening in deep learning industries and deep learning academia institutions.", 'start': 240.438, 'duration': 9.367}, {'end': 261.214, 'text': 'Finally, the main purpose of this course is we want to build a community here at MIT that is devoted to advancing the state of artificial intelligence,', 'start': 251.606, 'duration': 9.608}, {'end': 262.495, 'text': 'advancing a state of deep learning.', 'start': 261.214, 'duration': 1.281}, {'end': 267.271, 'text': "As part of this course, we'll cover some of the limitations of these algorithms.", 'start': 263.669, 'duration': 3.602}, {'end': 268.311, 'text': 'There are many.', 'start': 267.791, 'duration': 0.52}], 'summary': 'Mit course aims to advance ai and deep learning, covering practical skills and cutting-edge research.', 'duration': 38.094, 'max_score': 230.217, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY230217.jpg'}, {'end': 453.132, 'src': 'embed', 'start': 410.4, 'weight': 6, 'content': [{'end': 417.848, 'text': "But we're going to be giving out some amazing prizes, so including some NVIDIA GPUs and Google Homes.", 'start': 410.4, 'duration': 7.448}, {'end': 423.003, 'text': "On Friday, you'll, like I said, give a one-minute pitch.", 'start': 420.301, 'duration': 2.702}, {'end': 429.468, 'text': "There's somewhat of an art to pitching your idea in just one minute, even though it's extremely short.", 'start': 423.964, 'duration': 5.504}, {'end': 432.591, 'text': 'So we will be holding you to a strict deadline of that one minute.', 'start': 430.009, 'duration': 2.582}, {'end': 441.238, 'text': "The second option is a little more boring, but you'll be able to write a one-page paper about any deep learning paper that you find interesting.", 'start': 434.813, 'duration': 6.425}, {'end': 445.602, 'text': "And really, that's if you can't do the project proposal, you can do that.", 'start': 442.119, 'duration': 3.483}, {'end': 451.551, 'text': 'This class has a lot of online resources.', 'start': 449.229, 'duration': 2.322}, {'end': 453.132, 'text': 'You can find support on Piazza.', 'start': 451.691, 'duration': 1.441}], 'summary': 'Pitch for prizes including nvidia gpus and google homes. one-minute pitch on friday, or one-page paper if unable to do project proposal.', 'duration': 42.732, 'max_score': 410.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY410400.jpg'}], 'start': 61.239, 'title': 'Deep learning successes and course overview', 'summary': "Discusses deep learning's success in the imagenet challenge, surpassing human-level accuracy, and its applications in vision systems and medical diagnosis. it also highlights a one-week deep learning course at mit emphasizing practical skills, research, fostering a community, and promoting creativity in ai and deep learning.", 'chapters': [{'end': 172.415, 'start': 61.239, 'title': 'Deep learning successes', 'summary': 'Discusses the success of deep learning, particularly in the imagenet challenge, with a deep learning-based system surpassing human-level accuracy, and its applications in building complex vision systems and diagnosing medical conditions from input images.', 'duration': 111.176, 'highlights': ['Deep learning-based system won the 2012 ImageNet competition, surpassing human-level accuracy and outperforming all other competitors and challenges.', 'Deep learning can be used to build complex vision systems and diagnose medical conditions from input images, with the capability to explain its decision-making process.', 'Deep neural networks can model sequences with temporally dependent data points, expanding its application beyond single images.']}, {'end': 478.929, 'start': 172.995, 'title': 'Deep learning course overview', 'summary': 'Introduces a one-week deep learning course at mit, covering the practical skills and state-of-the-art research, while emphasizing the goal to build a community devoted to advancing ai and deep learning, with a focus on creating new songs through algorithms, and providing a platform for students to present project proposals or write one-page papers.', 'duration': 305.934, 'highlights': ['The course aims to provide the foundation to understand and implement deep learning algorithms, with a focus on practical skills and cutting-edge research.', 'Students will create an algorithm that listens to hours of music, learns the underlying representation of notes, and builds brand new songs.', 'The course emphasizes the goal of building a community at MIT devoted to advancing the state of artificial intelligence and deep learning.', 'Students have the option to present project proposals and will be rewarded with prizes, including NVIDIA GPUs and Google Homes.', 'An alternative to the project proposal is to write a one-page paper about an interesting deep learning paper.']}], 'duration': 417.69, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY61239.jpg', 'highlights': ['Deep learning-based system won the 2012 ImageNet competition, surpassing human-level accuracy and outperforming all other competitors and challenges.', 'Deep learning can be used to build complex vision systems and diagnose medical conditions from input images, with the capability to explain its decision-making process.', 'Deep neural networks can model sequences with temporally dependent data points, expanding its application beyond single images.', 'The course aims to provide the foundation to understand and implement deep learning algorithms, with a focus on practical skills and cutting-edge research.', 'The course emphasizes the goal of building a community at MIT devoted to advancing the state of artificial intelligence and deep learning.', 'Students will create an algorithm that listens to hours of music, learns the underlying representation of notes, and builds brand new songs.', 'Students have the option to present project proposals and will be rewarded with prizes, including NVIDIA GPUs and Google Homes.', 'An alternative to the project proposal is to write a one-page paper about an interesting deep learning paper.']}, {'end': 821.433, 'segs': [{'end': 552.886, 'src': 'embed', 'start': 529.027, 'weight': 0, 'content': [{'end': 537.014, 'text': 'So what deep learning tries to do is learn these features directly from data as opposed to being hand engineered by the human.', 'start': 529.027, 'duration': 7.987}, {'end': 540.596, 'text': 'That is, can we learn?', 'start': 538.975, 'duration': 1.621}, {'end': 548.183, 'text': 'if we want to learn to detect faces, can we first learn automatically from data that to detect faces we first need to detect edges in the image?', 'start': 540.596, 'duration': 7.587}, {'end': 552.886, 'text': 'compose these edges together to detect eyes and ears.', 'start': 549.181, 'duration': 3.705}], 'summary': 'Deep learning learns features from data to detect faces, edges, eyes, and ears.', 'duration': 23.859, 'max_score': 529.027, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY529027.jpg'}, {'end': 622.367, 'src': 'embed', 'start': 598.857, 'weight': 1, 'content': [{'end': 610.562, 'text': 'Second, these algorithms are massively parallelizable and can benefit tremendously from modern GPU architectures that simply just did not exist just more than a decade ago.', 'start': 598.857, 'duration': 11.705}, {'end': 620.406, 'text': 'And finally, due to open source toolboxes like TensorFlow, building and deploying these algorithms has become so streamlined, so simple,', 'start': 611.562, 'duration': 8.844}, {'end': 622.367, 'text': 'that we can teach it in a one week course like this.', 'start': 620.406, 'duration': 1.961}], 'summary': 'Algorithms benefit from modern gpus and streamlined deployment with tensorflow.', 'duration': 23.51, 'max_score': 598.857, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY598857.jpg'}, {'end': 682.126, 'src': 'embed', 'start': 651.971, 'weight': 2, 'content': [{'end': 655.192, 'text': 'We define a set of inputs x1 through xm on the left,', 'start': 651.971, 'duration': 3.221}, {'end': 664.494, 'text': 'And all we do is we multiply each of these inputs by their corresponding weight theta 1 through theta m, which are those arrows?', 'start': 657.85, 'duration': 6.644}, {'end': 675.361, 'text': 'We take this weighted combination of all of our inputs, sum them up, and pass them through a nonlinear activation function.', 'start': 665.835, 'duration': 9.526}, {'end': 678.764, 'text': 'And that produces our output y.', 'start': 677.243, 'duration': 1.521}, {'end': 679.344, 'text': "It's that simple.", 'start': 678.764, 'duration': 0.58}, {'end': 682.126, 'text': 'So we have m inputs, one output number.', 'start': 679.404, 'duration': 2.722}], 'summary': 'Inputs x1 through xm are multiplied by weights theta 1 through theta m, producing one output number.', 'duration': 30.155, 'max_score': 651.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY651971.jpg'}, {'end': 731.955, 'src': 'embed', 'start': 703.05, 'weight': 3, 'content': [{'end': 710.576, 'text': 'and this just represents some way that we can allow our model to learn or we can allow our activation function to shift to the left or right.', 'start': 703.05, 'duration': 7.526}, {'end': 718.503, 'text': 'So it allows us to, when we have no input features, to still provide a positive output.', 'start': 711.097, 'duration': 7.406}, {'end': 730.274, 'text': 'So on this equation on the right, we can actually rewrite this using linear algebra and dot products to make this a lot cleaner.', 'start': 721.71, 'duration': 8.564}, {'end': 731.955, 'text': "So let's do that.", 'start': 731.334, 'duration': 0.621}], 'summary': 'Discussing ways to shift activation function, rewrite equations using linear algebra.', 'duration': 28.905, 'max_score': 703.05, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY703050.jpg'}, {'end': 806.406, 'src': 'embed', 'start': 782.264, 'weight': 5, 'content': [{'end': 789.75, 'text': 'And this is a function that takes its input, any real number on the x-axis, and transforms it to an output between 0 and 1.', 'start': 782.264, 'duration': 7.486}, {'end': 797.257, 'text': 'And because all outputs of this function are between 0 and 1, it makes it a very popular choice in deep learning to represent probabilities.', 'start': 789.75, 'duration': 7.507}, {'end': 804.806, 'text': 'In fact, there are many types of nonlinear activation functions in deep neural networks.', 'start': 800.444, 'duration': 4.362}, {'end': 806.406, 'text': 'And here are some of the common ones.', 'start': 805.366, 'duration': 1.04}], 'summary': 'A function transforms real numbers to outputs between 0 and 1, popular in deep learning for representing probabilities.', 'duration': 24.142, 'max_score': 782.264, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY782264.jpg'}], 'start': 479.09, 'title': 'Deep learning fundamentals and neural network basics', 'summary': 'Discusses the growing importance of deep learning due to its ability to learn features directly from data, and its relevance in the big data environment. it also explains neural network basics, including computing output, bias, linear algebra, activation functions, and mentions the use of tensorflow for labs.', 'chapters': [{'end': 650.67, 'start': 479.09, 'title': 'Deep learning fundamentals', 'summary': 'Discusses the reasons for the growing importance of deep learning, emphasizing its ability to learn features directly from data and its relevance due to the big data environment, parallel processing capabilities, and streamlined deployment through open source toolboxes like tensorflow.', 'duration': 171.58, 'highlights': ['Deep learning recognizes the need to learn features directly from data instead of relying on pre-programmed features, enabling it to represent different levels of abstraction in the data.', 'The growing importance of deep learning is attributed to the prevalence of big data, modern GPU architectures, and streamlined deployment through open source toolboxes like TensorFlow.', 'The chapter introduces the fundamental building block of deep learning, the perceptron, as a single neuron in a neural network.']}, {'end': 821.433, 'start': 651.971, 'title': 'Neural network basics', 'summary': 'Explains the basics of neural networks, including the process of computing the output, the inclusion of bias, the use of linear algebra, the role of the activation function, and popular types of activation functions. it also mentions the use of tensorflow for labs.', 'duration': 169.462, 'highlights': ['The process of computing the output involves multiplying the inputs by their corresponding weights, summing the weighted combination, and passing it through a nonlinear activation function to produce the output y.', 'The inclusion of a bias term in the equation allows the model to learn and the activation function to shift, providing a positive output even with no input features.', 'The explanation involves the use of linear algebra and dot products to rewrite the equation for a cleaner representation.', 'An example of a popular activation function, the sigmoid function, is presented, which transforms any real number to an output between 0 and 1, making it suitable for representing probabilities.', 'The chapter mentions the use of TensorFlow for labs, providing a link between the material in lectures and the practical implementation in labs.']}], 'duration': 342.343, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY479090.jpg', 'highlights': ['Deep learning learns features directly from data, enabling abstraction representation', 'Growing importance due to big data prevalence, modern GPU architectures, and TensorFlow', 'Computing output involves multiplying inputs by weights, summing, and passing through activation function', 'Inclusion of bias term allows model to learn and activation function to shift', 'Explanation involves linear algebra and dot products for cleaner representation', 'Sigmoid function transforms any real number to an output between 0 and 1']}, {'end': 1523.043, 'segs': [{'end': 847.564, 'src': 'embed', 'start': 823.253, 'weight': 0, 'content': [{'end': 830.481, 'text': 'So the sigmoid activation function, which I talked about in the previous slide, now on the left, is just a function.', 'start': 823.253, 'duration': 7.228}, {'end': 834.122, 'text': "like I said, it's commonly used to produce probability outputs.", 'start': 830.481, 'duration': 3.641}, {'end': 838.203, 'text': 'Each of these activation functions has their own advantages and disadvantages.', 'start': 834.722, 'duration': 3.481}, {'end': 842.964, 'text': 'On the right, a very common activation function is the rectified linear unit, or relu.', 'start': 838.763, 'duration': 4.201}, {'end': 847.564, 'text': "This function is very popular because it's extremely simple to compute.", 'start': 844.024, 'duration': 3.54}], 'summary': 'Sigmoid and relu are common activation functions with their own advantages and disadvantages.', 'duration': 24.311, 'max_score': 823.253, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY823253.jpg'}, {'end': 902.748, 'src': 'embed', 'start': 869.65, 'weight': 1, 'content': [{'end': 870.97, 'text': 'Why do we need the activation function?', 'start': 869.65, 'duration': 1.32}, {'end': 875.372, 'text': 'Activation functions introduce non-linearities into the network.', 'start': 871.871, 'duration': 3.501}, {'end': 879.493, 'text': "That's the whole point of why activations themselves are non-linear.", 'start': 875.412, 'duration': 4.081}, {'end': 886.656, 'text': 'We want to model nonlinear data in the world because the world is extremely nonlinear.', 'start': 881.172, 'duration': 5.484}, {'end': 894.822, 'text': "Let's suppose I gave you this plot green and red points and I asked you to draw a single line, not a curve,", 'start': 888.017, 'duration': 6.805}, {'end': 898.224, 'text': 'just a line between the green and red points to separate them perfectly.', 'start': 894.822, 'duration': 3.402}, {'end': 902.748, 'text': "You'd find this really difficult, and probably you could get as best as something like this.", 'start': 898.885, 'duration': 3.863}], 'summary': 'Activation functions introduce non-linearities to model nonlinear data in the world.', 'duration': 33.098, 'max_score': 869.65, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY869650.jpg'}, {'end': 1122.847, 'src': 'embed', 'start': 1095.137, 'weight': 2, 'content': [{'end': 1098.319, 'text': "Now, if there's a few things that you learned from this class, let this be one of them.", 'start': 1095.137, 'duration': 3.182}, {'end': 1100.239, 'text': "And we'll keep repeating it over and over.", 'start': 1098.759, 'duration': 1.48}, {'end': 1106.702, 'text': 'In deep learning, you do a dot product, you apply a bias, and you add your non-linearity.', 'start': 1101.46, 'duration': 5.242}, {'end': 1112.525, 'text': 'You keep repeating that many, many times for each node, each neuron in your neural network.', 'start': 1107.263, 'duration': 5.262}, {'end': 1114.166, 'text': "And that's a neural network.", 'start': 1113.326, 'duration': 0.84}, {'end': 1117.585, 'text': "So let's simplify this diagram a little.", 'start': 1116.144, 'duration': 1.441}, {'end': 1122.847, 'text': 'I remove the bias since we are going to always have that and we just take it for granted from now on.', 'start': 1117.925, 'duration': 4.922}], 'summary': 'In deep learning, you do a dot product, apply a bias, and add non-linearity repeatedly for each neuron in a neural network.', 'duration': 27.71, 'max_score': 1095.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1095137.jpg'}, {'end': 1169.59, 'src': 'embed', 'start': 1142.637, 'weight': 4, 'content': [{'end': 1145.979, 'text': "If we want to define a multi-output perceptron, it's very simple.", 'start': 1142.637, 'duration': 3.342}, {'end': 1147.339, 'text': 'We just add another perceptron.', 'start': 1146.039, 'duration': 1.3}, {'end': 1149.84, 'text': 'Now, we have two outputs, y1 and y2.', 'start': 1148, 'duration': 1.84}, {'end': 1158.064, 'text': 'Each one has weight vector theta corresponding to the weight of each of the inputs.', 'start': 1150.821, 'duration': 7.243}, {'end': 1165.688, 'text': "Now, let's suppose we want to go the next step deeper.", 'start': 1162.847, 'duration': 2.841}, {'end': 1169.59, 'text': 'We want to create now a single-layered neural network.', 'start': 1166.889, 'duration': 2.701}], 'summary': 'Introduction to multi-output perceptron with two outputs and the intention to create a single-layered neural network.', 'duration': 26.953, 'max_score': 1142.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1142637.jpg'}, {'end': 1359.521, 'src': 'embed', 'start': 1327.659, 'weight': 5, 'content': [{'end': 1330.039, 'text': 'One feature is the number of lectures that you attend.', 'start': 1327.659, 'duration': 2.38}, {'end': 1333.64, 'text': 'Second feature is the number of hours that you spend on your final project.', 'start': 1330.779, 'duration': 2.861}, {'end': 1337.723, 'text': "Let's plot this data in our feature space.", 'start': 1335.421, 'duration': 2.302}, {'end': 1342.087, 'text': 'We plot green points are people who pass, red points are people that fail.', 'start': 1338.504, 'duration': 3.583}, {'end': 1353.717, 'text': 'And we want to know, given a new person, this guy, they spent five hours on their final project and went to four lectures.', 'start': 1343.108, 'duration': 10.609}, {'end': 1359.521, 'text': 'We want to know, did that person pass or fail the class? And we want to build a neural network that will determine this.', 'start': 1354.437, 'duration': 5.084}], 'summary': 'Neural network to predict student success based on lectures and project hours.', 'duration': 31.862, 'max_score': 1327.659, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1327659.jpg'}, {'end': 1428.821, 'src': 'embed', 'start': 1399.903, 'weight': 6, 'content': [{'end': 1402.325, 'text': "So we can't expect it to solve a problem it knows nothing about.", 'start': 1399.903, 'duration': 2.422}, {'end': 1408.868, 'text': 'So to tackle this problem of training a neural network, we have to first define a couple of things.', 'start': 1403.405, 'duration': 5.463}, {'end': 1410.249, 'text': "So first, we'll talk about the loss.", 'start': 1408.908, 'duration': 1.341}, {'end': 1421.916, 'text': 'The loss of a network basically tells our algorithm or our model how wrong our predictions are from the ground truth.', 'start': 1411.71, 'duration': 10.206}, {'end': 1428.821, 'text': 'So you can think of this as a distance between our predicted output and our actual output.', 'start': 1423.837, 'duration': 4.984}], 'summary': 'To train a neural network, we need to define the loss, which measures the error in predictions.', 'duration': 28.918, 'max_score': 1399.903, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1399903.jpg'}, {'end': 1476.636, 'src': 'embed', 'start': 1447.759, 'weight': 7, 'content': [{'end': 1452.002, 'text': "Now let's assume we're not given just one data point, one student, but we're given a whole class of students.", 'start': 1447.759, 'duration': 4.243}, {'end': 1455.705, 'text': 'So as previous data, I used this entire class from last year.', 'start': 1452.563, 'duration': 3.142}, {'end': 1463.29, 'text': "And if we want to quantify what's called the empirical loss now we care about how the model did on average over the entire data set,", 'start': 1456.265, 'duration': 7.025}, {'end': 1465.732, 'text': 'not for just a single student, but across the entire data set.', 'start': 1463.29, 'duration': 2.442}, {'end': 1466.913, 'text': 'And how we do that is very simple.', 'start': 1465.752, 'duration': 1.161}, {'end': 1469.375, 'text': 'We just take the average of the loss of each data point.', 'start': 1466.953, 'duration': 2.422}, {'end': 1473.378, 'text': "If we have n students, that's the average over n data points.", 'start': 1470.015, 'duration': 3.363}, {'end': 1476.636, 'text': 'This has other names besides empirical loss.', 'start': 1474.755, 'duration': 1.881}], 'summary': 'Quantify model performance over entire class using empirical loss, averaging loss of each data point.', 'duration': 28.877, 'max_score': 1447.759, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1447759.jpg'}], 'start': 823.253, 'title': 'Activation functions and neural networks', 'summary': 'Discusses the importance of activation functions in introducing non-linearities into neural networks, enabling the modeling of complex functions and decision boundaries, with a focus on the sigmoid and relu functions, and illustrates the composition of perceptrons into neural networks.', 'chapters': [{'end': 1301.739, 'start': 823.253, 'title': 'Activation functions and neural networks', 'summary': 'Discusses the importance of activation functions in introducing non-linearities into neural networks, enabling the modeling of complex functions and decision boundaries, with a focus on the sigmoid and relu functions, and illustrates the composition of perceptrons into neural networks.', 'duration': 478.486, 'highlights': ['Activation functions introduce non-linearities into the network, enabling the modeling of complex functions and decision boundaries.', 'The rectified linear unit (relu) activation function is popular due to its simplicity and piecewise linear nature.', 'The importance of using activation functions to enable the modeling of nonlinear data in the world.', 'The composition of perceptrons into neural networks involves the repeated application of dot product, bias addition, and non-linearity.', 'Illustration of the transition from single perceptrons to multi-output perceptrons, and subsequently to single-layered and deep neural networks.']}, {'end': 1523.043, 'start': 1302.48, 'title': 'Neural networks for pass/fail prediction', 'summary': "Introduces the application of neural networks to predict a student's likelihood of passing a class based on the number of lectures attended and hours spent on the final project, highlighting the need for network training and discussing the concept of empirical loss for evaluating model performance.", 'duration': 220.563, 'highlights': ["The network attempts to predict a student's likelihood of passing a class based on the number of lectures attended and hours spent on the final project.", "The need for training the neural network is emphasized, as an untrained network incorrectly predicts a student's probability of passing the class.", "The concept of empirical loss is introduced, which evaluates the model's performance by considering the average loss over an entire dataset of students."]}], 'duration': 699.79, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY823253.jpg', 'highlights': ['The rectified linear unit (relu) activation function is popular due to its simplicity and piecewise linear nature.', 'Activation functions introduce non-linearities into the network, enabling the modeling of complex functions and decision boundaries.', 'The composition of perceptrons into neural networks involves the repeated application of dot product, bias addition, and non-linearity.', 'The importance of using activation functions to enable the modeling of nonlinear data in the world.', 'Illustration of the transition from single perceptrons to multi-output perceptrons, and subsequently to single-layered and deep neural networks.', "The network attempts to predict a student's likelihood of passing a class based on the number of lectures attended and hours spent on the final project.", "The need for training the neural network is emphasized, as an untrained network incorrectly predicts a student's probability of passing the class.", "The concept of empirical loss is introduced, which evaluates the model's performance by considering the average loss over an entire dataset of students."]}, {'end': 1918.846, 'segs': [{'end': 1561.592, 'src': 'embed', 'start': 1524.608, 'weight': 0, 'content': [{'end': 1532.518, 'text': "Now, instead of predicting a single 1 or 0 output, yes or no, let's suppose we want to predict a continuous valued function.", 'start': 1524.608, 'duration': 7.91}, {'end': 1539.106, 'text': "Not will I pass this class, but what's the grade that I will get? And as a percentage, let's say, 0 to 100.", 'start': 1532.958, 'duration': 6.148}, {'end': 1543.972, 'text': "Now we're no longer limited to 0 to 1, but can actually output any real number on the number line.", 'start': 1539.106, 'duration': 4.866}, {'end': 1548.523, 'text': 'Now, instead of using cross entropy, we might want to use a different loss.', 'start': 1545.481, 'duration': 3.042}, {'end': 1551.745, 'text': "And for this let's think of something like a mean squared error loss.", 'start': 1548.943, 'duration': 2.802}, {'end': 1558.17, 'text': "where, as you're predicted and your true output diverge from each other, the loss increases as a quadratic function.", 'start': 1551.745, 'duration': 6.425}, {'end': 1561.592, 'text': 'OK, great.', 'start': 1561.192, 'duration': 0.4}], 'summary': 'Predict continuous function output using mean squared error loss.', 'duration': 36.984, 'max_score': 1524.608, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1524608.jpg'}, {'end': 1725.366, 'src': 'embed', 'start': 1692.929, 'weight': 3, 'content': [{'end': 1699.232, 'text': 'So we negate our gradient and we adjust our weight such that we step in the opposite direction of that gradient,', 'start': 1692.929, 'duration': 6.303}, {'end': 1707.136, 'text': 'such that we move continuously towards the lowest point in this landscape until we finally converge at a local minima.', 'start': 1699.232, 'duration': 7.904}, {'end': 1708.036, 'text': 'And then we just stop.', 'start': 1707.236, 'duration': 0.8}, {'end': 1711.578, 'text': "So let's summarize this with some pseudocode.", 'start': 1709.837, 'duration': 1.741}, {'end': 1713.018, 'text': 'So we randomly initialize our weights.', 'start': 1711.598, 'duration': 1.42}, {'end': 1715.78, 'text': 'We loop until convergence the following.', 'start': 1714.019, 'duration': 1.761}, {'end': 1725.366, 'text': 'We compute the gradient at that point, and then simply we apply this update rule, where the update takes as input the negative gradient.', 'start': 1716.445, 'duration': 8.921}], 'summary': 'Algorithm updates weights with negative gradient to converge at local minima.', 'duration': 32.437, 'max_score': 1692.929, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1692929.jpg'}, {'end': 1873.577, 'src': 'embed', 'start': 1842.802, 'weight': 1, 'content': [{'end': 1857.031, 'text': 'This is because z1, our hidden state, is only dependent on our previous input x and that single weight theta 1.', 'start': 1842.802, 'duration': 14.229}, {'end': 1868.515, 'text': 'Now the process of backpropagation is basically you repeat this process over and over again for every weight in your network until you compute that gradient dj d theta.', 'start': 1857.031, 'duration': 11.484}, {'end': 1873.577, 'text': 'And you can use that as part of your optimization process to find your local minima.', 'start': 1869.155, 'duration': 4.422}], 'summary': 'Backpropagation repeats process for every weight to compute gradient for optimization.', 'duration': 30.775, 'max_score': 1842.802, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1842802.jpg'}, {'end': 1922.728, 'src': 'embed', 'start': 1898.446, 'weight': 4, 'content': [{'end': 1907.072, 'text': "This is an illustration or a visualization of the landscape, like I've plotted before, but of a real deep neural network of ResNet-50, to be precise.", 'start': 1898.446, 'duration': 8.626}, {'end': 1912.384, 'text': 'This was actually taken from a paper published about a month ago.', 'start': 1908.442, 'duration': 3.942}, {'end': 1918.846, 'text': 'The authors attempt to visualize the lost landscape to show how difficult gradient descent can actually be.', 'start': 1912.804, 'duration': 6.042}, {'end': 1922.728, 'text': "So there's a possibility that you can get lost in any one of these local minima.", 'start': 1919.266, 'duration': 3.462}], 'summary': "Visualization of resnet-50's deep neural network landscape reveals challenges in gradient descent.", 'duration': 24.282, 'max_score': 1898.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1898446.jpg'}], 'start': 1524.608, 'title': 'Training neural network with continuous valued function and backpropagation', 'summary': 'Covers the shift to predicting continuous valued functions, mean squared error loss, and the process of training neural networks along with the explanation of backpropagation for computing gradients, emphasizing the application of chain rule and addressing the complexities of training modern deep neural network architectures.', 'chapters': [{'end': 1760.639, 'start': 1524.608, 'title': 'Training neural network with continuous valued function', 'summary': 'Discusses the shift from predicting binary outputs to continuous valued functions, introducing mean squared error loss, and the process of training a neural network by minimizing the loss through gradient descent.', 'duration': 236.031, 'highlights': ['The shift from predicting binary outputs to continuous valued functions', 'Introduction of mean squared error loss', 'Process of training a neural network by minimizing the loss through gradient descent']}, {'end': 1918.846, 'start': 1761.459, 'title': 'Backpropagation and gradient computation', 'summary': 'Explains the process of backpropagation for computing gradients in neural networks, emphasizing the application of chain rule, and touches on the complexities of training modern deep neural network architectures.', 'duration': 157.387, 'highlights': ['The process of backpropagation in neural networks involves computing the gradient of the loss function with respect to each weight, utilizing the chain rule to backpropagate gradients through the network, ultimately aiding in the optimization process to find local minima.', 'Modern deep neural network architectures are depicted as extremely non-convex, as illustrated by the landscape visualization of a real deep neural network, ResNet-50, highlighting the challenges of gradient descent in practice.', 'The backpropagation process involves repeatedly applying the chain rule for every weight in the network, backpropagating gradients through layers, and computing the gradient dj d theta to aid in the optimization process.']}], 'duration': 394.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1524608.jpg', 'highlights': ['Introduction of mean squared error loss', 'The process of backpropagation in neural networks involves computing the gradient of the loss function with respect to each weight, utilizing the chain rule to backpropagate gradients through the network, ultimately aiding in the optimization process to find local minima.', 'The shift from predicting binary outputs to continuous valued functions', 'Process of training a neural network by minimizing the loss through gradient descent', 'Modern deep neural network architectures are depicted as extremely non-convex, as illustrated by the landscape visualization of a real deep neural network, ResNet-50, highlighting the challenges of gradient descent in practice.', 'The backpropagation process involves repeatedly applying the chain rule for every weight in the network, backpropagating gradients through layers, and computing the gradient dj d theta to aid in the optimization process.']}, {'end': 2558.952, 'segs': [{'end': 1966.884, 'src': 'embed', 'start': 1937.401, 'weight': 3, 'content': [{'end': 1942.783, 'text': 'But this basically determines how large of a step we take in the direction of our gradient.', 'start': 1937.401, 'duration': 5.382}, {'end': 1948.085, 'text': "And in practice, setting this learning rate, it's just a number, but setting it can be very difficult.", 'start': 1943.263, 'duration': 4.822}, {'end': 1956.269, 'text': 'If we set the learning rate too low, then the model may get stuck in a local minima and may never actually find its way out of that local minima.', 'start': 1949.006, 'duration': 7.263}, {'end': 1961.171, 'text': "Because at the bottom of the local minima, obviously your gradient is zero, so it's just going to stop moving.", 'start': 1957.049, 'duration': 4.122}, {'end': 1966.884, 'text': 'If I set the learning rate too large, it could overshoot and actually diverge.', 'start': 1962.54, 'duration': 4.344}], 'summary': 'Setting the learning rate is crucial in gradient descent to avoid getting stuck or overshooting.', 'duration': 29.483, 'max_score': 1937.401, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1937401.jpg'}, {'end': 2025.755, 'src': 'embed', 'start': 2000.398, 'weight': 1, 'content': [{'end': 2007.081, 'text': 'How about we try to build an adaptive algorithm that changes its learning rate as training happens?', 'start': 2000.398, 'duration': 6.683}, {'end': 2011.644, 'text': "So this is a learning rate that actually adapts to the landscape that it's in.", 'start': 2008.062, 'duration': 3.582}, {'end': 2014.705, 'text': 'So the learning rate is no longer a fixed number.', 'start': 2012.384, 'duration': 2.321}, {'end': 2015.305, 'text': 'It can change.', 'start': 2014.745, 'duration': 0.56}, {'end': 2016.246, 'text': 'It can go up and down.', 'start': 2015.345, 'duration': 0.901}, {'end': 2025.755, 'text': 'And this will change depending on the location that the update is currently at the gradient in that location.', 'start': 2017.392, 'duration': 8.363}], 'summary': 'Propose building an adaptive algorithm with a variable learning rate based on the landscape, allowing it to change during training.', 'duration': 25.357, 'max_score': 2000.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY2000398.jpg'}, {'end': 2166.463, 'src': 'embed', 'start': 2120.468, 'weight': 0, 'content': [{'end': 2126.034, 'text': "Instead, let's create a variant of this algorithm called stochastic gradient descent,", 'start': 2120.468, 'duration': 5.566}, {'end': 2129.237, 'text': 'where we compute the gradient just using a single training example.', 'start': 2126.034, 'duration': 3.203}, {'end': 2134.623, 'text': "Now, this is nice because it's really easy to compute the gradient for a single training example.", 'start': 2130.499, 'duration': 4.124}, {'end': 2137.547, 'text': "It's not nearly as intense as over the entire training set.", 'start': 2134.663, 'duration': 2.884}, {'end': 2141.889, 'text': 'But as the name might suggest, this is a more stochastic estimate.', 'start': 2138.407, 'duration': 3.482}, {'end': 2143.33, 'text': "It's much more noisy.", 'start': 2142.409, 'duration': 0.921}, {'end': 2147.832, 'text': "It can make us jump around the landscape in ways that we didn't anticipate.", 'start': 2143.35, 'duration': 4.482}, {'end': 2152.055, 'text': "It doesn't actually represent the true gradient of our data set, because it's only a single point.", 'start': 2147.852, 'duration': 4.203}, {'end': 2154.036, 'text': "So what's the middle ground?", 'start': 2153.135, 'duration': 0.901}, {'end': 2159.899, 'text': 'How about we define a mini batch of b data points,', 'start': 2154.696, 'duration': 5.203}, {'end': 2166.463, 'text': 'compute the average gradient across those b data points and actually use that as an estimate of our true gradient?', 'start': 2159.899, 'duration': 6.564}], 'summary': 'Algorithm: stochastic gradient descent computes the gradient using a single training example, with a more stochastic and noisy estimate, but a middle ground is to use a mini batch of b data points for computing the average gradient.', 'duration': 45.995, 'max_score': 2120.468, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY2120468.jpg'}, {'end': 2300.389, 'src': 'embed', 'start': 2258.725, 'weight': 5, 'content': [{'end': 2265.529, 'text': 'Ideally, in machine learning, we want a model that accurately describes our test data, not our training data, but our test data.', 'start': 2258.725, 'duration': 6.804}, {'end': 2276.164, 'text': 'Said differently, we want to build models that can learn representations from our training data and still generalize well on unseen test data.', 'start': 2267.59, 'duration': 8.574}, {'end': 2279.565, 'text': 'Assume you want to build a line to describe these points.', 'start': 2277.284, 'duration': 2.281}, {'end': 2287.789, 'text': 'Underfitting describes the process on the left, where the complexity of our model is simply not high enough to capture the nuances of our data.', 'start': 2280.446, 'duration': 7.343}, {'end': 2296.629, 'text': "If we go to over 50 on the right, we're actually having too complex of a model and actually just memorizing our training data,", 'start': 2289.127, 'duration': 7.502}, {'end': 2300.389, 'text': "which means that if we introduce a new test data point, it's not going to generalize well.", 'start': 2296.629, 'duration': 3.76}], 'summary': 'In machine learning, models should generalize well to test data, avoiding underfitting and overfitting.', 'duration': 41.664, 'max_score': 2258.725, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY2258725.jpg'}, {'end': 2354.21, 'src': 'embed', 'start': 2329.691, 'weight': 2, 'content': [{'end': 2335.436, 'text': "And, as we've seen before, this is extremely critical, because we don't want our data,", 'start': 2329.691, 'duration': 5.745}, {'end': 2340.741, 'text': "we don't want our models to just memorize data and only do well in our training set.", 'start': 2335.436, 'duration': 5.305}, {'end': 2349.668, 'text': 'One of the most popular techniques for regularization in neural networks is dropout.', 'start': 2344.685, 'duration': 4.983}, {'end': 2351.529, 'text': 'This is an extremely simple idea.', 'start': 2349.948, 'duration': 1.581}, {'end': 2354.21, 'text': "Let's revisit this picture of a deep neural network.", 'start': 2352.209, 'duration': 2.001}], 'summary': 'Regularization in neural networks is critical to prevent memorization of data, with dropout being a popular technique.', 'duration': 24.519, 'max_score': 2329.691, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY2329691.jpg'}], 'start': 1919.266, 'title': 'Optimizing learning rates and overfitting in ml', 'summary': 'Covers challenges in setting learning rates, adaptive learning rates, stochastic gradient descent, and benefits of mini-batching. it also discusses overfitting, model generalization, and techniques like regularization, dropout, and early stopping to improve model performance.', 'chapters': [{'end': 2234.978, 'start': 1919.266, 'title': 'Optimizing learning rates and mini-batching', 'summary': 'Discusses the challenges in setting the learning rate for gradient descent, the concept of adaptive learning rates, the implementation of stochastic gradient descent and the benefits of mini-batching in deep neural networks.', 'duration': 315.712, 'highlights': ['The challenge of setting the learning rate for gradient descent, which can lead to getting stuck in local minima or overshooting and diverging if not properly adjusted.', 'The concept of adaptive learning rates that change during training to adapt to the landscape, and the existence of numerous algorithms for computing adaptive learning rates.', 'The implementation of stochastic gradient descent using a single training example, providing a less intense but more noisy estimate of the gradient.', 'The benefits of mini-batching by computing the average gradient across a batch of data points, leading to faster computation, more accurate gradient estimation, and parallelizable computation.']}, {'end': 2558.952, 'start': 2237.22, 'title': 'Overfitting and regularization', 'summary': 'Discusses the concept of overfitting in machine learning, emphasizing the importance of model generalization and introducing techniques like regularization, including dropout and early stopping, to combat overfitting and improve model performance on unseen test data.', 'duration': 321.732, 'highlights': ['Regularization techniques like dropout and early stopping are introduced to combat overfitting and improve model generalization.', 'The concept of overfitting is explained, illustrating the consequences of models being either too simplistic or too complex, leading to poor generalization on test data.', 'The importance of building models that can accurately generalize on unseen test data is emphasized, highlighting the need to avoid memorizing training data.']}], 'duration': 639.686, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/JN6H4rQvwgY/pics/JN6H4rQvwgY1919266.jpg', 'highlights': ['The benefits of mini-batching by computing the average gradient across a batch of data points, leading to faster computation, more accurate gradient estimation, and parallelizable computation.', 'The concept of adaptive learning rates that change during training to adapt to the landscape, and the existence of numerous algorithms for computing adaptive learning rates.', 'Regularization techniques like dropout and early stopping are introduced to combat overfitting and improve model generalization.', 'The challenge of setting the learning rate for gradient descent, which can lead to getting stuck in local minima or overshooting and diverging if not properly adjusted.', 'The implementation of stochastic gradient descent using a single training example, providing a less intense but more noisy estimate of the gradient.', 'The concept of overfitting is explained, illustrating the consequences of models being either too simplistic or too complex, leading to poor generalization on test data.', 'The importance of building models that can accurately generalize on unseen test data is emphasized, highlighting the need to avoid memorizing training data.']}], 'highlights': ['Deep learning-based system won the 2012 ImageNet competition, surpassing human-level accuracy and outperforming all other competitors and challenges.', 'The rectified linear unit (relu) activation function is popular due to its simplicity and piecewise linear nature.', 'The benefits of mini-batching by computing the average gradient across a batch of data points, leading to faster computation, more accurate gradient estimation, and parallelizable computation.', 'The process of backpropagation in neural networks involves computing the gradient of the loss function with respect to each weight, utilizing the chain rule to backpropagate gradients through the network, ultimately aiding in the optimization process to find local minima.', 'Deep learning learns features directly from data, enabling abstraction representation', 'Introduction of mean squared error loss', 'The chapter highlights the capability of building intelligent algorithms capable of solving very complex problems, which were not feasible to solve a decade ago.', 'The concept of adaptive learning rates that change during training to adapt to the landscape, and the existence of numerous algorithms for computing adaptive learning rates.', 'The chapter defines intelligence as the ability to process information for informing future predictions and decisions, providing a high-level understanding of the concept.', 'The composition of perceptrons into neural networks involves the repeated application of dot product, bias addition, and non-linearity.']}