title

MIT 6.S191 (2019): Introduction to Deep Learning

description

MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
January 2019
For all lectures, slides and lab materials: http://introtodeeplearning.com

detail

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This is a one-week intensive boot camp on everything deep learning.', 'start': 25.719, 'duration': 6.005}, {'end': 37.484, 'text': "You'll get up close and personal with some of the foundations of the algorithms driving this remarkable field.", 'start': 32.5, 'duration': 4.984}, {'end': 44.67, 'text': "And you'll actually learn how to build some intelligent algorithms capable of solving incredibly complex problems.", 'start': 38.205, 'duration': 6.465}, {'end': 55.979, 'text': 'So, over the past couple of years, deep learning has revolutionized many aspects of research and industry, including things like autonomous vehicles,', 'start': 46.651, 'duration': 9.328}, {'end': 61.162, 'text': 'medicine and health care, reinforcement learning, generative modeling,', 'start': 55.979, 'duration': 5.183}, {'end': 67.526, 'text': 'robotics and a whole host of other applications like natural language processing, finance and security.', 'start': 61.162, 'duration': 6.364}], 'summary': "Mit's 6s191 is a one-week intensive boot camp on deep learning, now in its third year, with updates and applications in various fields.", 'duration': 63.846, 'max_score': 3.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs3680.jpg'}, {'end': 201.322, 'src': 'embed', 'start': 167.128, 'weight': 3, 'content': [{'end': 170.771, 'text': 'We have an amazing set of lectures lined up for you this week, including today,', 'start': 167.128, 'duration': 3.643}, {'end': 177.817, 'text': "which will kick off an introduction on neural networks and sequence-based modeling, which you'll hear about in the second part of the class.", 'start': 170.771, 'duration': 7.046}, {'end': 185.015, 'text': "Tomorrow, we'll cover some stuff about computer vision and deep generative modeling.", 'start': 179.332, 'duration': 5.683}, {'end': 201.322, 'text': "And the day after that we'll talk even about reinforcement learning and end on some of the challenges and limitations of the current deep learning approaches and kind of touch on how we can move forward as a field past these challenges.", 'start': 186.455, 'duration': 14.867}], 'summary': 'Exciting lectures this week: neural networks, computer vision, and reinforcement learning. discussing challenges and future advancements in deep learning.', 'duration': 34.194, 'max_score': 167.128, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs167128.jpg'}, {'end': 254.346, 'src': 'embed', 'start': 214.139, 'weight': 4, 'content': [{'end': 218.12, 'text': 'We have speakers from Nvidia, IBM, Google coming to give talks.', 'start': 214.139, 'duration': 3.981}, {'end': 220.301, 'text': 'So I highly recommend attending these as well.', 'start': 218.52, 'duration': 1.781}, {'end': 227.703, 'text': 'And finally, the class will conclude with some final project presentations from students like you in the audience,', 'start': 221.681, 'duration': 6.022}, {'end': 230.464, 'text': "where you'll present some final projects for this class.", 'start': 227.703, 'duration': 2.761}, {'end': 233.105, 'text': "And then we'll end on an award ceremony to celebrate.", 'start': 231.024, 'duration': 2.081}, {'end': 239.633, 'text': 'So as you might have seen or heard already, this class is offered for credit.', 'start': 235.529, 'duration': 4.104}, {'end': 241.014, 'text': 'You can take this class for grade.', 'start': 239.673, 'duration': 1.341}, {'end': 245.578, 'text': "And if you're taking this class for grade, you have two options to fulfill your grade requirement.", 'start': 241.575, 'duration': 4.003}, {'end': 252.925, 'text': 'First option is that you can actually do a project proposal where you will present your project on the final day of class.', 'start': 246.539, 'duration': 6.386}, {'end': 254.346, 'text': "That's what I was saying before on Friday.", 'start': 252.965, 'duration': 1.381}], 'summary': 'Speakers from nvidia, ibm, google giving talks and student project presentations for credit.', 'duration': 40.207, 'max_score': 214.139, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs214139.jpg'}], 'start': 3.68, 'title': 'Mit and ai classes', 'summary': "Introduces mit's one-week intensive boot camp on deep learning, entering its third year, and outlines the concepts of artificial intelligence, machine learning, and deep learning, with details on the schedule and grading options for the class.", 'chapters': [{'end': 67.526, 'start': 3.68, 'title': 'Mit deep learning boot camp', 'summary': "Introduces mit's official course on introduction to deep learning, highlighting it as a one-week intensive boot camp covering the foundations and applications of deep learning, with the course entering its third year.", 'duration': 63.846, 'highlights': ['The course is a one-week intensive boot camp on everything deep learning, offering up-close insights into the foundations of the algorithms driving the field and practical experience in building intelligent algorithms (quantifiable data: duration of the course).', 'The course is in its third year, indicating its established presence and continuous improvement over time (quantifiable data: duration of the course).', 'Deep learning has revolutionized various industries and research fields such as autonomous vehicles, medicine, reinforcement learning, generative modeling, robotics, natural language processing, finance, and security, showcasing its wide-ranging impact (quantifiable data: examples of industries and research fields impacted).']}, {'end': 289.8, 'start': 68.467, 'title': 'Deep learning and ai class', 'summary': 'Introduces the concepts of artificial intelligence, machine learning, and deep learning, emphasizing the goal of teaching algorithms to learn tasks from raw data, and outlines the schedule and grading options for the class.', 'duration': 221.333, 'highlights': ['The class focuses on 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its third year, indicating its established presence and continuous improvement over time (duration: three years).', 'Introduction to neural networks, sequence-based modeling, computer vision, deep generative modeling, and reinforcement learning are covered in the upcoming lectures.', 'Guest lecturers from top AI researchers, including speakers from Nvidia, IBM, and Google, are scheduled to give talks.', 'The class is offered for credit with options for fulfilling grade requirements through a project proposal and presentation.']}, {'end': 590.004, 'segs': [{'end': 318.299, 'src': 'embed', 'start': 289.8, 'weight': 0, 'content': [{'end': 294.866, 'text': "just so that you're forced to really think about what is the core idea that you want to present to us on Friday.", 'start': 289.8, 'duration': 5.066}, {'end': 307.151, 'text': "Your presentations will be judged by a panel of judges, and we'll be awarding GPUs and some Google Home AI assistance.", 'start': 296.383, 'duration': 10.768}, {'end': 312.054, 'text': "This year, we're offering three NVIDIA GPUs, each one worth over $1, 000.", 'start': 307.691, 'duration': 4.363}, {'end': 318.299, 'text': 'As some of you know, these GPUs are the backbone of doing cutting edge deep learning research.', 'start': 312.054, 'duration': 6.245}], 'summary': 'Presentations judged by a panel for gpus worth over $1,000 each', 'duration': 28.499, 'max_score': 289.8, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs289800.jpg'}, {'end': 358.877, 'src': 'embed', 'start': 330.189, 'weight': 1, 'content': [{'end': 336.274, 'text': "if you don't want to do the project presentation but you still want to receive credit for this class, you can do the second option,", 'start': 330.189, 'duration': 6.085}, {'end': 338.476, 'text': 'which is a little more boring in my opinion.', 'start': 336.274, 'duration': 2.202}, {'end': 341.98, 'text': 'But you can write a one-page review of a deep learning paper.', 'start': 339.057, 'duration': 2.923}, {'end': 345.073, 'text': 'And this will be due on the last day of class.', 'start': 343.132, 'duration': 1.941}, {'end': 350.454, 'text': "And this is for people that don't want to do the project presentation, but you still want to get credit for this class.", 'start': 345.853, 'duration': 4.601}, {'end': 358.877, 'text': "Please post to Piazza if you have questions about the labs that we'll be doing today or any of the future days.", 'start': 353.535, 'duration': 5.342}], 'summary': 'Option to write a one-page review of a deep learning paper for credit, due on the last day of class.', 'duration': 28.688, 'max_score': 330.189, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs330189.jpg'}, {'end': 393.583, 'src': 'embed', 'start': 365.858, 'weight': 6, 'content': [{'end': 370.159, 'text': 'along with announcements, digital recordings, as well as slides for these classes.', 'start': 365.858, 'duration': 4.301}, {'end': 373.859, 'text': "Today's slides are already released, so you can find everything online.", 'start': 370.579, 'duration': 3.28}, {'end': 379.921, 'text': 'And of course, if you have any questions, you can email us at intro2deeplearning-staff at mit.edu.', 'start': 374.7, 'duration': 5.221}, {'end': 385.762, 'text': 'This course has an incredible team that you can reach out to in case you have any questions or issues about anything.', 'start': 380.441, 'duration': 5.321}, {'end': 387.702, 'text': "So please don't hesitate to reach out.", 'start': 386.362, 'duration': 1.34}, {'end': 393.583, 'text': 'And finally, we want to give a huge thanks to all of the sponsors that made this course possible.', 'start': 389.242, 'duration': 4.341}], 'summary': "Intro to deep learning course has released today's slides online and offers support via email. thanks to sponsors.", 'duration': 27.725, 'max_score': 365.858, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs365858.jpg'}, {'end': 432.782, 'src': 'embed', 'start': 407.584, 'weight': 2, 'content': [{'end': 418.011, 'text': 'Well, traditional machine learning algorithms typically define sets of rules or features that you want to extract from the data.', 'start': 407.584, 'duration': 10.427}, {'end': 422.895, 'text': 'Usually these are hand engineered features and they tend to be extremely brittle in practice.', 'start': 418.572, 'duration': 4.323}, {'end': 432.782, 'text': "Now the key idea or the key insight of deep learning is that let's not hand engineer these features, instead let's learn them directly from raw data.", 'start': 423.575, 'duration': 9.207}], 'summary': 'Deep learning avoids hand-engineered features, learning directly from raw data.', 'duration': 25.198, 'max_score': 407.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs407584.jpg'}, {'end': 545.59, 'src': 'embed', 'start': 481.36, 'weight': 3, 'content': [{'end': 488.184, 'text': "So why are we studying this now? Well, for one, data has become so prevalent in today's society.", 'start': 481.36, 'duration': 6.824}, {'end': 489.965, 'text': "We're living in the age of big data.", 'start': 488.204, 'duration': 1.761}, {'end': 493.329, 'text': 'where we have more access to data than ever before.', 'start': 490.967, 'duration': 2.362}, {'end': 495.611, 'text': 'And these models are hungry for data.', 'start': 493.429, 'duration': 2.182}, {'end': 498.014, 'text': 'So we need to feed them with all the data.', 'start': 496.072, 'duration': 1.942}, {'end': 503.839, 'text': 'And a lot of these data sets that we have available, like computer vision data sets, natural language processing data sets.', 'start': 498.074, 'duration': 5.765}, {'end': 508.083, 'text': 'this raw amount of data was just not available when these algorithms were created.', 'start': 503.839, 'duration': 4.244}, {'end': 515.655, 'text': 'Second, these algorithms are massively parallelizable at their core.', 'start': 509.454, 'duration': 6.201}, {'end': 519.878, 'text': "At their most fundamental building blocks, as you'll learn today, they're massively parallelizable.", 'start': 515.816, 'duration': 4.062}, {'end': 526.02, 'text': 'And this means that they can benefit tremendously from very specialized hardware, such as GPUs.', 'start': 520.438, 'duration': 5.582}, {'end': 535.343, 'text': 'And again, technology like these GPUs simply did not exist in the decades that deep learning or the foundations of deep learning were developed.', 'start': 526.94, 'duration': 8.403}, {'end': 541.527, 'text': "And finally, due to open source toolboxes like TensorFlow, which you'll learn to use in this class,", 'start': 536.363, 'duration': 5.164}, {'end': 545.59, 'text': 'building and deploying these models has become more streamlined than ever before.', 'start': 541.527, 'duration': 4.063}], 'summary': 'Studying data due to its prevalence, hunger for data, parallelizability, and streamlined deployment.', 'duration': 64.23, 'max_score': 481.36, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs481360.jpg'}], 'start': 289.8, 'title': 'Deep learning in projects and patterns', 'summary': 'Covers upcoming project presentations with awards of three nvidia gpus worth over $1,000 each and an alternative option for credit, while also discussing the key idea of deep learning, its relevance in big data, parallelizability of algorithms, and the process of building and deploying models using open source toolboxes like tensorflow.', 'chapters': [{'end': 407.544, 'start': 289.8, 'title': 'Deep learning project presentation', 'summary': 'Discusses the upcoming project presentations, offering three nvidia gpus worth over $1,000 each as awards, and an alternative option of writing a one-page review of a deep learning paper to receive credit for the class, along with guidance on accessing course materials and support channels.', 'duration': 117.744, 'highlights': ['Three NVIDIA GPUs worth over $1,000 each will be awarded as prizes for the project presentations.', 'An alternative option is available to write a one-page review of a deep learning paper to receive credit for the class.', 'Guidance is provided on accessing course materials and support channels, including the availability of slides, digital recordings, and contact information.']}, {'end': 590.004, 'start': 407.584, 'title': 'Deep learning: unlocking complex patterns', 'summary': 'Discusses the key idea of deep learning, its relevance in the age of big data, the parallelizability of algorithms with specialized hardware, and the streamlined process of building and deploying models using open source toolboxes like tensorflow.', 'duration': 182.42, 'highlights': ['The chapter explains the key insight of deep learning - learning features directly from raw data, in contrast to hand-engineered features, making the models less brittle in practice.', 'It discusses the relevance of deep learning in the age of big data, where there is more access to data than ever before, and the models benefit from this abundance of data.', 'The chapter highlights the importance of specialized hardware, such as GPUs, and how the parallelizability of algorithms can benefit tremendously from them.', 'It emphasizes the streamlined process of building and deploying models using open source toolboxes like TensorFlow, making it increasingly easy to abstract away details and solve complex problems.']}], 'duration': 300.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs289800.jpg', 'highlights': ['Three NVIDIA GPUs worth over $1,000 each will be awarded as prizes for the project presentations.', 'An alternative option is available to write a one-page review of a deep learning paper to receive credit for the class.', 'The chapter explains the key insight of deep learning - learning features directly from raw data, in contrast to hand-engineered features, making the models less brittle in practice.', 'The chapter highlights the importance of specialized hardware, such as GPUs, and how the parallelizability of algorithms can benefit tremendously from them.', 'It discusses the relevance of deep learning in the age of big data, where there is more access to data than ever before, and the models benefit from this abundance of data.', 'It emphasizes the streamlined process of building and deploying models using open source toolboxes like TensorFlow, making it increasingly easy to abstract away details and solve complex problems.', 'Guidance is provided on accessing course materials and support channels, including the availability of slides, digital recordings, and contact information.']}, {'end': 1460.821, 'segs': [{'end': 619.428, 'src': 'embed', 'start': 590.965, 'weight': 0, 'content': [{'end': 597.731, 'text': "Let's start by talking about and describing the feedforward propagation of information through that model.", 'start': 590.965, 'duration': 6.766}, {'end': 603.756, 'text': 'We define a set of inputs, x1 through xm, which you can see on the left-hand side.', 'start': 599.232, 'duration': 4.524}, {'end': 610.422, 'text': 'And each of these inputs are actually multiplied by a corresponding weight, w1 through wm.', 'start': 605.277, 'duration': 5.145}, {'end': 617.148, 'text': 'So you can imagine if you have x1, you multiply it by w1.', 'start': 613.567, 'duration': 3.581}, {'end': 619.428, 'text': 'You have x2, you multiply it by w2, and so on.', 'start': 617.168, 'duration': 2.26}], 'summary': 'Described feedforward propagation model with inputs x1 through xm and weights w1 through wm.', 'duration': 28.463, 'max_score': 590.965, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs590965.jpg'}, {'end': 745.281, 'src': 'embed', 'start': 718.163, 'weight': 3, 'content': [{'end': 723.807, 'text': 'What is it? So one common example of a nonlinear activation function is called the sigmoid function.', 'start': 718.163, 'duration': 5.644}, {'end': 727.129, 'text': 'And you can see one here defined on the bottom right.', 'start': 724.267, 'duration': 2.862}, {'end': 735.19, 'text': 'This is a function that takes as input any real number and outputs a new number between 0 and 1.', 'start': 727.77, 'duration': 7.42}, {'end': 740.115, 'text': "So you can see it's essentially collapsing your input between this range of 0 and 1.", 'start': 735.19, 'duration': 4.925}, {'end': 745.281, 'text': 'This is just one example of an activation function, but there are many, many, many activation functions used in neural networks.', 'start': 740.115, 'duration': 5.166}], 'summary': 'Sigmoid function collapses input to 0-1 range, a common nonlinear activation function in neural networks.', 'duration': 27.118, 'max_score': 718.163, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs718163.jpg'}, {'end': 865.693, 'src': 'embed', 'start': 839.404, 'weight': 1, 'content': [{'end': 845.589, 'text': 'So, activation functions, the purpose of activation functions, is to introduce nonlinearities into the network.', 'start': 839.404, 'duration': 6.185}, {'end': 854.67, 'text': 'This is extremely important in deep learning, or in machine learning in general, because in real life, data is almost always very nonlinear.', 'start': 846.547, 'duration': 8.123}, {'end': 858.711, 'text': 'Imagine I told you to separate here the green from the red points.', 'start': 855.49, 'duration': 3.221}, {'end': 865.693, 'text': "You might think that's easy, but then what if I told you you had to only use a single line to do it? Well, now it's impossible.", 'start': 859.531, 'duration': 6.162}], 'summary': 'Activation functions introduce nonlinearities in deep learning to handle real-life nonlinear data.', 'duration': 26.289, 'max_score': 839.404, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs839404.jpg'}, {'end': 930.336, 'src': 'embed', 'start': 899.921, 'weight': 5, 'content': [{'end': 902.863, 'text': "And that's exactly what makes neural networks so powerful in practice.", 'start': 899.921, 'duration': 2.942}, {'end': 906.455, 'text': "So let's understand this with a simple example.", 'start': 904.773, 'duration': 1.682}, {'end': 911.799, 'text': 'Imagine I give you a trained network with weights w on the top here.', 'start': 907.375, 'duration': 4.424}, {'end': 914.081, 'text': 'So w0 is 1.', 'start': 911.959, 'duration': 2.122}, {'end': 917.564, 'text': "And let's see, w0 is 1.", 'start': 914.081, 'duration': 3.483}, {'end': 920.447, 'text': 'The w vector is 3, negative 2.', 'start': 917.564, 'duration': 2.883}, {'end': 922.449, 'text': 'So this is a trained neural network.', 'start': 920.447, 'duration': 2.002}, {'end': 926.412, 'text': 'And I want to feed in a new input to this network.', 'start': 923.249, 'duration': 3.163}, {'end': 930.336, 'text': "Well, how do we compute the output? Remember from before, it's the dot product.", 'start': 926.833, 'duration': 3.503}], 'summary': 'Neural networks compute output using dot product, demonstrated with trained network and weights w0=1, w=[3, -2].', 'duration': 30.415, 'max_score': 899.921, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs899921.jpg'}, {'end': 1066.564, 'src': 'embed', 'start': 1040.147, 'weight': 6, 'content': [{'end': 1046.675, 'text': "But when we're dealing with small dimensional input data, like here we're dealing with only two dimensions, we can make these beautiful plots.", 'start': 1040.147, 'duration': 6.528}, {'end': 1054.004, 'text': 'And these are very valuable in actually visualizing the learning algorithm, visualizing how our output is relating to our input.', 'start': 1047.316, 'duration': 6.688}, {'end': 1059.069, 'text': "We're going to find very soon that we can't really do this for all problems,", 'start': 1054.584, 'duration': 4.485}, {'end': 1066.564, 'text': "because While here we're dealing with only two inputs in practical applications in deep neural networks, we're going to be dealing with hundreds,", 'start': 1059.069, 'duration': 7.495}], 'summary': 'Visualizing learning algorithm with 2d input data is valuable, but not practical for deep neural networks with hundreds of inputs.', 'duration': 26.417, 'max_score': 1040.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1040146.jpg'}, {'end': 1178.934, 'src': 'heatmap', 'start': 1140.732, 'weight': 0.861, 'content': [{'end': 1146.255, 'text': 'If we want to define a multi-output neural network, now all we have to do is add another perceptron to this picture.', 'start': 1140.732, 'duration': 5.523}, {'end': 1148.856, 'text': 'Now we have two outputs.', 'start': 1147.956, 'duration': 0.9}, {'end': 1152.618, 'text': 'Each one is a normal perceptron like we defined before, nothing extra.', 'start': 1149.136, 'duration': 3.482}, {'end': 1157.76, 'text': 'And each one is taking all the inputs from the left-hand side, computing this weighted sum,', 'start': 1153.398, 'duration': 4.362}, {'end': 1162.588, 'text': 'adding a bias and passing it through an activation function.', 'start': 1159.147, 'duration': 3.441}, {'end': 1164.529, 'text': "Let's keep going.", 'start': 1163.989, 'duration': 0.54}, {'end': 1167.39, 'text': "Now let's take a look at a single layered neural network.", 'start': 1165.269, 'duration': 2.121}, {'end': 1171.371, 'text': 'This is one where we have a single hidden layer between our inputs and our outputs.', 'start': 1168.01, 'duration': 3.361}, {'end': 1178.934, 'text': 'We call it a hidden layer because unlike the input and the output, which are strictly observable, our hidden layer is learned.', 'start': 1171.971, 'duration': 6.963}], 'summary': 'Defining a multi-output neural network with two outputs and a single hidden layer.', 'duration': 38.202, 'max_score': 1140.732, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1140732.jpg'}, {'end': 1342.892, 'src': 'embed', 'start': 1312.658, 'weight': 7, 'content': [{'end': 1314.259, 'text': "So now let's keep building on this idea.", 'start': 1312.658, 'duration': 1.601}, {'end': 1316.161, 'text': 'Now we want to build a deep neural network.', 'start': 1314.46, 'duration': 1.701}, {'end': 1317.202, 'text': 'What is a deep neural network?', 'start': 1316.201, 'duration': 1.001}, {'end': 1324.507, 'text': "Well, it's just one where we keep stacking these hidden layers back to back, to back to back, to create increasingly deeper and deeper models.", 'start': 1317.222, 'duration': 7.285}, {'end': 1332.066, 'text': 'one where the output is computed by going deeper into the network and computing these weighted sums over and over and over again,', 'start': 1325.822, 'duration': 6.244}, {'end': 1334.187, 'text': 'with these activation functions repeatedly applied.', 'start': 1332.066, 'duration': 2.121}, {'end': 1337.249, 'text': 'So this is awesome.', 'start': 1336.568, 'duration': 0.681}, {'end': 1342.892, 'text': 'Now we have an idea on how to actually build a neural network from scratch, going all the way from a single perceptron.', 'start': 1337.329, 'duration': 5.563}], 'summary': 'Building a deep neural network involves stacking hidden layers to create increasingly deeper models, computed by repeating weighted sums and activation functions.', 'duration': 30.234, 'max_score': 1312.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1312658.jpg'}], 'start': 590.965, 'title': 'Neural networks and feedforward propagation', 'summary': 'Explains feedforward propagation in a neural network, detailing weighted summation, nonlinear activation functions, and the significance of introducing nonlinearities, with examples. it also covers building neural networks from scratch, progressing from single neuron perceptrons to single-layer and deep neural networks, and includes an example of training a network to predict class passing probability, resulting in a 10% prediction for a specific case.', 'chapters': [{'end': 1037.866, 'start': 590.965, 'title': 'Neural network feedforward propagation', 'summary': 'Explains the feedforward propagation in a neural network, detailing the process of weighted summation, application of nonlinear activation functions, and the significance of activation functions in introducing nonlinearities to handle complex data, illustrated with examples and visualizations.', 'duration': 446.901, 'highlights': ['The process of feedforward propagation involves multiplying inputs by corresponding weights, summing the products, and passing the result through a nonlinear activation function to produce the final output.', 'The introduction of nonlinear activation functions is crucial in neural networks to handle complex, nonlinear data, enabling the approximation of complex functions and the drawing of complex decision boundaries in the feature space.', 'The use of activation functions is necessary to introduce nonlinearities into the network, as linear activation functions limit the network to producing linear decision boundaries, while nonlinear activation functions allow for complex decision boundaries.', 'The sigmoid function and ReLU function are common examples of activation functions used in neural networks, with the sigmoid function suitable for modeling probabilities and the ReLU function popular for its simplicity and ability to capture great properties of activation functions.', 'The feedforward propagation process can be illustrated and understood using visualizations in the feature space, showcasing the computation of the output and the impact of nonlinear activation functions on dividing the space into hyperplanes.']}, {'end': 1460.821, 'start': 1040.147, 'title': 'Building neural networks from scratch', 'summary': 'Covers building neural networks from scratch, starting with single neuron perceptrons, then progressing to single-layer and deep neural networks. it also includes an example of training a neural network to predict class passing probability, with a resulting prediction of 10% for a specific case.', 'duration': 420.674, 'highlights': ['Overview of Building Neural Networks', 'Example of Training a Neural Network', 'Challenges in Visualization for High-Dimensional Input Data']}], 'duration': 869.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs590965.jpg', 'highlights': ['The process of feedforward propagation involves multiplying inputs by corresponding weights, summing the products, and passing the result through a nonlinear activation function to produce the final output.', 'The introduction of nonlinear activation functions is crucial in neural networks to handle complex, nonlinear data, enabling the approximation of complex functions and the drawing of complex decision boundaries in the feature space.', 'The use of activation functions is necessary to introduce nonlinearities into the network, as linear activation functions limit the network to producing linear decision boundaries, while nonlinear activation functions allow for complex decision boundaries.', 'The sigmoid function and ReLU function are common examples of activation functions used in neural networks, with the sigmoid function suitable for modeling probabilities and the ReLU function popular for its simplicity and ability to capture great properties of activation functions.', 'The feedforward propagation process can be illustrated and understood using visualizations in the feature space, showcasing the computation of the output and the impact of nonlinear activation functions on dividing the space into hyperplanes.', 'Example of Training a Neural Network', 'Challenges in Visualization for High-Dimensional Input Data', 'Overview of Building Neural Networks']}, {'end': 1943.067, 'segs': [{'end': 1626.609, 'src': 'heatmap', 'start': 1517.024, 'weight': 4, 'content': [{'end': 1524.671, 'text': "So let's assume that we have data not just from one student now, but we have data from many, many different students passing and failing the class.", 'start': 1517.024, 'duration': 7.647}, {'end': 1533.068, 'text': 'We now care about how this model does not just on that one student, but across the entire population of students.', 'start': 1526.684, 'duration': 6.384}, {'end': 1535.489, 'text': 'And we call this the empirical loss.', 'start': 1533.608, 'duration': 1.881}, {'end': 1538.491, 'text': "And that's just the mean of all of the losses for the individual students.", 'start': 1535.529, 'duration': 2.962}, {'end': 1545.075, 'text': 'We can do it by literally just computing the loss for each of these students and taking their mean.', 'start': 1539.312, 'duration': 5.763}, {'end': 1547.417, 'text': 'When training a network.', 'start': 1546.296, 'duration': 1.121}, {'end': 1554.661, 'text': 'what we really want to do is not minimize the loss for any particular student, but we want to minimize the loss across the entire training set.', 'start': 1547.417, 'duration': 7.244}, {'end': 1564.87, 'text': "So if we go back to our problem on predicting if you'll pass or fail the class, this is a problem of binary classification.", 'start': 1557.782, 'duration': 7.088}, {'end': 1566.913, 'text': 'Your output is 0 or 1.', 'start': 1564.971, 'duration': 1.942}, {'end': 1571.839, 'text': "We already learned that when outputs are 0 or 1, you're probably going to want to use a softmax output.", 'start': 1566.913, 'duration': 4.926}, {'end': 1581.102, 'text': "For those of you who aren't familiar with cross entropy, this was an idea introduced actually at MIT in a master's thesis here over 50 years ago.", 'start': 1573.24, 'duration': 7.862}, {'end': 1585.503, 'text': "It's widely used in different areas like thermodynamics, and we use it here in machine learning as well.", 'start': 1581.122, 'duration': 4.381}, {'end': 1587.203, 'text': "It's used all over information theory.", 'start': 1585.723, 'duration': 1.48}, {'end': 1593.265, 'text': 'And what this is doing here is essentially computing the loss between this 0,', 'start': 1588.404, 'duration': 4.861}, {'end': 1598.986, 'text': '1 output and the true output that the student either passed or failed the class.', 'start': 1593.265, 'duration': 5.721}, {'end': 1605.833, 'text': "Let's suppose instead of computing a 0, 1 output, now we want to compute the actual grade that you will get on the class.", 'start': 1600.029, 'duration': 5.804}, {'end': 1608.295, 'text': "So now it's not 0, 1, but it's actually a grade.", 'start': 1606.194, 'duration': 2.101}, {'end': 1616.541, 'text': 'It could be any number actually, right? Now we want to use a different loss because the output of our neural network is different.', 'start': 1608.996, 'duration': 7.545}, {'end': 1620.985, 'text': 'And defining losses is actually kind of one of the arts in deep learning.', 'start': 1617.522, 'duration': 3.463}, {'end': 1626.609, 'text': "So you have to define the questions that you're asking so that you can define the loss that you need to optimize over.", 'start': 1621.045, 'duration': 5.564}], 'summary': 'In machine learning, minimizing loss across all students is crucial for training a network and depends on the type of output.', 'duration': 109.585, 'max_score': 1517.024, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1517024.jpg'}, {'end': 1593.265, 'src': 'embed', 'start': 1564.971, 'weight': 2, 'content': [{'end': 1566.913, 'text': 'Your output is 0 or 1.', 'start': 1564.971, 'duration': 1.942}, {'end': 1571.839, 'text': "We already learned that when outputs are 0 or 1, you're probably going to want to use a softmax output.", 'start': 1566.913, 'duration': 4.926}, {'end': 1581.102, 'text': "For those of you who aren't familiar with cross entropy, this was an idea introduced actually at MIT in a master's thesis here over 50 years ago.", 'start': 1573.24, 'duration': 7.862}, {'end': 1585.503, 'text': "It's widely used in different areas like thermodynamics, and we use it here in machine learning as well.", 'start': 1581.122, 'duration': 4.381}, {'end': 1587.203, 'text': "It's used all over information theory.", 'start': 1585.723, 'duration': 1.48}, {'end': 1593.265, 'text': 'And what this is doing here is essentially computing the loss between this 0,', 'start': 1588.404, 'duration': 4.861}], 'summary': 'Softmax output is used for 0 or 1 outputs. cross entropy widely used in machine learning and information theory.', 'duration': 28.294, 'max_score': 1564.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1564971.jpg'}, {'end': 1668.942, 'src': 'embed', 'start': 1627.901, 'weight': 1, 'content': [{'end': 1633.127, 'text': "So here in this example, since we're not optimizing over a 0, 1 loss, we're optimizing over any real number,", 'start': 1627.901, 'duration': 5.226}, {'end': 1635.51, 'text': "we're going to use a mean squared error loss.", 'start': 1633.127, 'duration': 2.383}, {'end': 1638.514, 'text': "And that's just computing the squared error.", 'start': 1635.771, 'duration': 2.743}, {'end': 1643.32, 'text': 'So you take the difference between what you expect the output to be and what your actual output was.', 'start': 1638.634, 'duration': 4.686}, {'end': 1647.605, 'text': 'You take that difference, you square it, and you compute the mean over your entire population.', 'start': 1643.84, 'duration': 3.765}, {'end': 1650.557, 'text': 'OK, great.', 'start': 1650.157, 'duration': 0.4}, {'end': 1653.419, 'text': "So now let's put some of this information together.", 'start': 1651.038, 'duration': 2.381}, {'end': 1654.96, 'text': "We've learned how to build neural networks.", 'start': 1653.499, 'duration': 1.461}, {'end': 1657.121, 'text': "We've learned how to quantify their loss.", 'start': 1655.52, 'duration': 1.601}, {'end': 1664.945, 'text': 'Now we can learn how to actually use that loss to iteratively update and train the neural network over time, given some data.', 'start': 1657.821, 'duration': 7.124}, {'end': 1668.942, 'text': 'And, essentially, what this amounts to.', 'start': 1667.341, 'duration': 1.601}], 'summary': 'Optimizing neural networks using mean squared error loss and iterative updates.', 'duration': 41.041, 'max_score': 1627.901, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1627901.jpg'}, {'end': 1913, 'src': 'heatmap', 'start': 1863.288, 'weight': 0, 'content': [{'end': 1866.149, 'text': 'But I never actually told you how to compute this term.', 'start': 1863.288, 'duration': 2.861}, {'end': 1870.211, 'text': 'This is actually a crucial part of deep learning and neural networks in general.', 'start': 1866.169, 'duration': 4.042}, {'end': 1875.554, 'text': 'Computing this term is essentially all that matters when you try and optimize your network.', 'start': 1870.831, 'duration': 4.723}, {'end': 1878.155, 'text': "It's the most computational part of training as well.", 'start': 1875.934, 'duration': 2.221}, {'end': 1880.656, 'text': "And it's known as back propagation.", 'start': 1879.175, 'duration': 1.481}, {'end': 1890.25, 'text': "We'll start with a very simple network with one input, one hidden layer, one hidden unit, and one output.", 'start': 1882.142, 'duration': 8.108}, {'end': 1903.042, 'text': 'Computing the gradient of our loss with respect to w2 corresponds to telling us how much a small change in w2 affects our output, our loss.', 'start': 1891.03, 'duration': 12.012}, {'end': 1913, 'text': 'So if we write this out as a derivative, we can start by computing this by simply expanding this derivative by using the chain rule.', 'start': 1905.492, 'duration': 7.508}], 'summary': 'Back propagation is crucial in deep learning, involving computational gradient computation for network optimization.', 'duration': 49.712, 'max_score': 1863.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1863288.jpg'}], 'start': 1460.841, 'title': 'Neural network training and optimization', 'summary': 'Discusses the training of neural networks by minimizing loss, focusing on empirical loss, binary classification, and various loss functions like cross entropy and mean squared error. it also covers the iterative update process using gradient descent, computation of gradients, learning rate, and the role of backpropagation in deep learning.', 'chapters': [{'end': 1647.605, 'start': 1460.841, 'title': 'Neural network training and loss functions', 'summary': 'Explains the concept of training neural networks by minimizing loss, with a focus on empirical loss, binary classification, and different loss functions, including cross entropy and mean squared error.', 'duration': 186.764, 'highlights': ["The empirical loss is the mean of all the losses for individual students, reflecting the model's performance across the entire population of students.", 'The concept of binary classification is discussed, emphasizing the use of softmax output and cross entropy loss for predicting 0 or 1 outputs.', 'The use of mean squared error loss is introduced for optimizing over real number outputs, involving computing the squared error and taking the mean over the entire population.']}, {'end': 1943.067, 'start': 1650.157, 'title': 'Neural network training and gradient descent', 'summary': 'Covers the process of iteratively updating and training a neural network to minimize empirical loss by using gradient descent, including the computation of the gradient, the use of learning rate, and the crucial role of backpropagation in deep learning.', 'duration': 292.91, 'highlights': ['The process of iteratively updating and training a neural network to minimize empirical loss using gradient descent is explained, involving the computation of the gradient and the use of learning rate.', 'The crucial role of backpropagation in deep learning and neural networks is emphasized, as it is essential in optimizing the network and is the most computational part of training.', 'The step-by-step process of computing the gradient through backpropagation for a simple network with one input, one hidden layer, one hidden unit, and one output is detailed.']}], 'duration': 482.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1460841.jpg', 'highlights': ['The crucial role of backpropagation in deep learning and neural networks is emphasized, as it is essential in optimizing the network and is the most computational part of training.', 'The process of iteratively updating and training a neural network to minimize empirical loss using gradient descent is explained, involving the computation of the gradient and the use of learning rate.', 'The concept of binary classification is discussed, emphasizing the use of softmax output and cross entropy loss for predicting 0 or 1 outputs.', 'The use of mean squared error loss is introduced for optimizing over real number outputs, involving computing the squared error and taking the mean over the entire population.', "The empirical loss is the mean of all the losses for individual students, reflecting the model's performance across the entire population of students.", 'The step-by-step process of computing the gradient through backpropagation for a simple network with one input, one hidden layer, one hidden unit, and one output is detailed.']}, {'end': 2378.377, 'segs': [{'end': 1967.064, 'src': 'embed', 'start': 1944.549, 'weight': 0, 'content': [{'end': 1952.275, 'text': 'We can take that middle term, now expand it out again using the same chain rule, and back propagate those gradients even further back in the network.', 'start': 1944.549, 'duration': 7.726}, {'end': 1958.339, 'text': 'And essentially we keep repeating this for every weight in the network,', 'start': 1955.077, 'duration': 3.262}, {'end': 1963.162, 'text': 'using the gradients for later layers to back-propagate those errors back into the original input.', 'start': 1958.339, 'duration': 4.823}, {'end': 1967.064, 'text': 'We do this for all of the weights, and that gives us our gradient for each weight.', 'start': 1963.462, 'duration': 3.602}], 'summary': 'Back-propagate gradients for all weights in the network to obtain the gradients for each weight.', 'duration': 22.515, 'max_score': 1944.549, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1944549.jpg'}, {'end': 2018.119, 'src': 'embed', 'start': 1980.852, 'weight': 1, 'content': [{'end': 1988.017, 'text': "So the question is, how do you ensure that this gives you a global minimum instead of a local minimum? So you don't.", 'start': 1980.852, 'duration': 7.165}, {'end': 1991.379, 'text': 'We have no guarantees that this is not a global minimum.', 'start': 1988.598, 'duration': 2.781}, {'end': 1996.923, 'text': 'The whole training of stochastic gradient descent is a greedy optimization algorithm.', 'start': 1993.081, 'duration': 3.842}, {'end': 2000.326, 'text': "So you're only taking this greedy approach and optimizing only a local minimum.", 'start': 1996.943, 'duration': 3.383}, {'end': 2006.696, 'text': "There are different ways, extensions of stochastic gradient descent that don't take a greedy approach.", 'start': 2001.175, 'duration': 5.521}, {'end': 2008.377, 'text': 'They take an adaptive approach.', 'start': 2007.116, 'duration': 1.261}, {'end': 2009.497, 'text': 'They look around a little bit.', 'start': 2008.397, 'duration': 1.1}, {'end': 2011.857, 'text': 'These are typically more expensive to compute.', 'start': 2009.997, 'duration': 1.86}, {'end': 2015.958, 'text': 'Stochastic gradient descent is extremely cheap to compute in practice.', 'start': 2011.877, 'duration': 4.081}, {'end': 2018.119, 'text': "And that's one of the reasons it's used.", 'start': 2016.658, 'duration': 1.461}], 'summary': 'Stochastic gradient descent is cheap but may only optimize local minimums.', 'duration': 37.267, 'max_score': 1980.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1980852.jpg'}, {'end': 2337.241, 'src': 'embed', 'start': 2312.423, 'weight': 4, 'content': [{'end': 2321.206, 'text': "So, instead of computing a noisy gradient of a single point, let's get a better estimate by batching our data into mini batches of B data points,", 'start': 2312.423, 'duration': 8.783}, {'end': 2322.267, 'text': 'capital B data points.', 'start': 2321.206, 'duration': 1.061}, {'end': 2328.549, 'text': 'So now this gives us an estimate of the true gradient by just averaging the gradient from each of these points.', 'start': 2323.067, 'duration': 5.482}, {'end': 2335.132, 'text': "This is great because now it's much easier to compute than full gradient descent.", 'start': 2330.45, 'duration': 4.682}, {'end': 2337.241, 'text': "It's a lot less points.", 'start': 2336.22, 'duration': 1.021}], 'summary': 'Using mini batches of b data points provides better estimate of true gradient, making computation easier and requiring fewer points.', 'duration': 24.818, 'max_score': 2312.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs2312423.jpg'}], 'start': 1944.549, 'title': 'Neural network training', 'summary': 'Discusses stochastic gradient descent, its limitations, and cost-effectiveness, while also highlighting the challenges of training neural networks, such as minimizing loss, setting the learning rate, and the benefits of batching data for gradient descent.', 'chapters': [{'end': 2018.119, 'start': 1944.549, 'title': 'Stochastic gradient descent', 'summary': "Discusses the process of back-propagating gradients through the network to obtain weights' gradients, along with the limitations of stochastic gradient descent and the potential for local minimum convergence, emphasizing the algorithm's cost-effectiveness and widespread usage.", 'duration': 73.57, 'highlights': ['The process of back-propagating gradients through the network and repeating it for every weight in the network provides the gradients for each weight, contributing to the optimization of the network (relevance score: 5)', 'Stochastic gradient descent is a greedy optimization algorithm that may only optimize a local minimum, and there are no guarantees that it will reach the global minimum (relevance score: 4)', 'Extensions of stochastic gradient descent take an adaptive approach, which is more expensive to compute but can potentially overcome the limitations of the greedy approach (relevance score: 3)', 'Stochastic gradient descent is widely used due to its cost-effectiveness in computation (relevance score: 2)']}, {'end': 2378.377, 'start': 2018.539, 'title': 'Training neural networks: insights & techniques', 'summary': 'Highlights the challenges of training neural networks in practice, including the difficulty in minimizing loss, the importance of setting the learning rate, and the benefits of batching data for gradient descent, which can lead to faster convergence and parallelizable computation.', 'duration': 359.838, 'highlights': ['The visualization of the lost landscape of a neural network shows the presence of many local minima, making it extremely difficult to find the optimal true minimum.', 'Setting the learning rate is crucial, as a too slow rate may result in the model getting stuck in local minima, while a too large rate can cause the gradient to explode, leading to divergence from the loss.', 'Batching data into mini batches for gradient descent allows for a more accurate estimate of the true gradient, leading to faster convergence and massively parallelizable computation.']}], 'duration': 433.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs1944549.jpg', 'highlights': ['The process of back-propagating gradients through the network contributes to network optimization (relevance score: 5)', 'Stochastic gradient descent may only optimize a local minimum, with no guarantee of reaching the global minimum (relevance score: 4)', 'Extensions of stochastic gradient descent take an adaptive approach to potentially overcome limitations (relevance score: 3)', 'Stochastic gradient descent is widely used due to its cost-effectiveness in computation (relevance score: 2)', 'Batching data for gradient descent allows for a more accurate estimate of the true gradient (relevance score: 1)']}, {'end': 2723.557, 'segs': [{'end': 2406.641, 'src': 'embed', 'start': 2380.138, 'weight': 2, 'content': [{'end': 2385.222, 'text': 'Now, the last topic I want to address before ending is this idea of overfitting.', 'start': 2380.138, 'duration': 5.084}, {'end': 2392.147, 'text': 'This is one of the most fundamental problems in machine learning as a whole, not just deep learning.', 'start': 2385.542, 'duration': 6.605}, {'end': 2399.498, 'text': 'And at its core, it involves understanding the complexity of your model.', 'start': 2394.036, 'duration': 5.462}, {'end': 2406.641, 'text': 'So you want to build a model that performs well and generalizes well, not just to your training set, but to your test set as well.', 'start': 2399.758, 'duration': 6.883}], 'summary': 'Overfitting is a crucial problem in machine learning, involving model complexity and generalization to training and test sets.', 'duration': 26.503, 'max_score': 2380.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs2380138.jpg'}, {'end': 2512.484, 'src': 'embed', 'start': 2485.854, 'weight': 0, 'content': [{'end': 2490.697, 'text': 'The most popular regularization technique in deep learning is a very simple idea called dropout.', 'start': 2485.854, 'duration': 4.843}, {'end': 2494.28, 'text': "Let's revisit this in a picture of a deep neural network again.", 'start': 2491.458, 'duration': 2.822}, {'end': 2501.084, 'text': 'In dropout, during training, we randomly set some of our activations of the hidden neurons to zero.', 'start': 2495.48, 'duration': 5.604}, {'end': 2502.698, 'text': 'with some probability.', 'start': 2501.857, 'duration': 0.841}, {'end': 2505.88, 'text': "That's why we call it dropping out, because we're essentially killing off those neurons.", 'start': 2502.978, 'duration': 2.902}, {'end': 2507.32, 'text': "So let's do that.", 'start': 2506.7, 'duration': 0.62}, {'end': 2509.602, 'text': 'So we kill off these random sample of neurons.', 'start': 2507.36, 'duration': 2.242}, {'end': 2512.484, 'text': "And now we've created a different pathway through the network.", 'start': 2510.142, 'duration': 2.342}], 'summary': 'Dropout is a popular regularization technique in deep learning, randomly setting hidden neuron activations to zero, creating different network pathways.', 'duration': 26.63, 'max_score': 2485.854, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs2485854.jpg'}, {'end': 2611.088, 'src': 'embed', 'start': 2544.885, 'weight': 1, 'content': [{'end': 2553.148, 'text': 'creates an ensemble of multiple models through the path of the network and is able to generalize better to unseen test data.', 'start': 2544.885, 'duration': 8.263}, {'end': 2563.024, 'text': "So the second technique for regularization is this notion that we'll talk about, which is early stopping.", 'start': 2555.669, 'duration': 7.355}, {'end': 2566.985, 'text': 'And the idea here is also extremely simple.', 'start': 2564.604, 'duration': 2.381}, {'end': 2573.046, 'text': "Let's train our neural network like before, no dropout, but let's just stop training before we have a chance to overfit.", 'start': 2567.285, 'duration': 5.761}, {'end': 2583.131, 'text': 'So we start training, and the definition of overfitting is just when our model starts to perform worse on the test set than on the training set.', 'start': 2574.226, 'duration': 8.905}, {'end': 2587.974, 'text': 'So we can start off, and we can plot how our loss is going for both the training and test set.', 'start': 2583.471, 'duration': 4.503}, {'end': 2590.336, 'text': 'We can see that both are decreasing, so we keep training.', 'start': 2588.234, 'duration': 2.102}, {'end': 2595.559, 'text': 'Now we can see that the training validation, both losses are kind of starting to plateau here.', 'start': 2591.016, 'duration': 4.543}, {'end': 2596.82, 'text': 'We can keep going.', 'start': 2596.06, 'duration': 0.76}, {'end': 2599.342, 'text': 'The training loss is always going to decay.', 'start': 2597.301, 'duration': 2.041}, {'end': 2601.663, 'text': "It's always going to keep decreasing, because,", 'start': 2599.522, 'duration': 2.141}, {'end': 2608.207, 'text': 'especially if you have a network that is having such a large capacity to essentially memorize your data,', 'start': 2601.663, 'duration': 6.544}, {'end': 2611.088, 'text': 'you can always perfectly get a training accuracy of zero.', 'start': 2608.207, 'duration': 2.881}], 'summary': 'Ensemble of models improves generalization; early stopping prevents overfitting by monitoring loss plateauing.', 'duration': 66.203, 'max_score': 2544.885, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs2544885.jpg'}], 'start': 2380.138, 'title': 'Managing overfitting in machine learning', 'summary': 'Covers overfitting in machine learning, highlighting the importance of building models that generalize well. it discusses regularization, focusing on dropout for deep neural networks, and emphasizes the strategy of early stopping to prevent overfitting and achieve optimal model performance.', 'chapters': [{'end': 2442.038, 'start': 2380.138, 'title': 'Understanding overfitting in machine learning', 'summary': 'Discusses the fundamental problem of overfitting in machine learning, emphasizing the importance of building a model that performs well and generalizes to both training and test sets, by avoiding underfitting or overfitting.', 'duration': 61.9, 'highlights': ['Overfitting is a fundamental problem in machine learning, involving the complexity of the model, and can lead to high generalization error when the model is too complex and essentially memorizes the training data.', "Underfitting occurs when the model's complexity is not large enough to learn the full complexity of the data, resulting in poor performance and generalization to both training and test sets."]}, {'end': 2573.046, 'start': 2443.001, 'title': 'Regularization for deep neural networks', 'summary': 'Discusses the importance of regularization for deep neural networks, particularly focusing on the popular technique of dropout, which involves randomly setting activations of hidden neurons to zero during training to create an ensemble of multiple models and improve generalization to unseen test data.', 'duration': 130.045, 'highlights': ['Dropout is a popular regularization technique in deep learning, where during training, random activations of hidden neurons are set to zero with some probability, creating an ensemble of different paths through the network, thus improving generalization to unseen test data.', 'Early stopping is another regularization technique that involves training the neural network without dropout, but stopping the training before overfitting occurs, thus preventing the model from becoming too complex to generalize well.']}, {'end': 2723.557, 'start': 2574.226, 'title': 'Overfitting and early stopping', 'summary': 'Introduces the concept of overfitting and the strategy of early stopping in training neural networks, emphasizing the importance of monitoring the training and validation losses to prevent overfitting and achieve optimal model performance.', 'duration': 149.331, 'highlights': ['The training loss always decreases, especially for deep neural networks with large capacity, potentially leading to overfitting if training continues for too long.', 'The validation set loss starts to increase as the training set loss continues to decrease, indicating the occurrence of overfitting.', 'The concept of early stopping involves monitoring the model during training and stopping when overfitting is detected, aiming to use the last model before overfitting occurs.']}], 'duration': 343.419, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5v1JnYv_yWs/pics/5v1JnYv_yWs2380138.jpg', 'highlights': ['Dropout is a popular regularization technique in deep learning, improving generalization to unseen test data.', 'The validation set loss starts to increase as the training set loss continues to decrease, indicating overfitting.', 'Overfitting is a fundamental problem in machine learning, leading to high generalization error when the model is too complex.', 'Early stopping involves training the neural network without dropout, preventing the model from becoming too complex to generalize well.', 'The training loss always decreases, potentially leading to overfitting if training continues for too long.']}], 'highlights': ['The course is a one-week intensive boot camp on everything deep learning, offering practical experience (duration: one week)', 'Deep learning has revolutionized various industries and research fields, showcasing its wide-ranging impact (examples: autonomous vehicles, medicine, robotics)', 'The course is in its third year, indicating its established presence and continuous improvement over time (duration: three years)', 'Introduction to neural networks, sequence-based modeling, computer vision, deep generative modeling, and reinforcement learning are covered in the upcoming lectures', 'Guest lecturers from top AI researchers, including speakers from Nvidia, IBM, and Google, are scheduled to give talks', 'The class is offered for credit with options for fulfilling grade requirements through a project proposal and presentation', 'Three NVIDIA GPUs worth over $1,000 each will be awarded as prizes for the project presentations', 'An alternative option is available to write a one-page review of a deep learning paper to receive credit for the class', 'The chapter explains the key insight of deep learning - learning features directly from raw data, in contrast to hand-engineered features, making the models less brittle in practice', 'The chapter highlights the importance of specialized hardware, such as GPUs, and how the parallelizability of algorithms can benefit tremendously from them', 'It discusses the relevance of deep learning in the age of big data, where there is more access to data than ever before, and the models benefit from this abundance of data', 'It emphasizes the streamlined process of building and deploying models using open source toolboxes like TensorFlow, making it increasingly easy to abstract away details and solve complex problems', 'The process of feedforward propagation involves multiplying inputs by corresponding weights, summing the products, and passing the result through a nonlinear activation function to produce the final output', 'The introduction of nonlinear activation functions is crucial in neural networks to handle complex, nonlinear data, enabling the approximation of complex functions and the drawing of complex decision boundaries in the feature space', 'The use of activation functions is necessary to introduce nonlinearities into the network, as linear activation functions limit the network to producing linear decision boundaries, while nonlinear activation functions allow for complex decision boundaries', 'The sigmoid function and ReLU function are common examples of activation functions used in neural networks, with the sigmoid function suitable for modeling probabilities and the ReLU function popular for its simplicity and ability to capture great properties of activation functions', 'The crucial role of backpropagation in deep learning and neural networks is emphasized, as it is essential in optimizing the network and is the most computational part of training', 'The process of iteratively updating and training a neural network to minimize empirical loss using gradient descent is explained, involving the computation of the gradient and the use of learning rate', 'The concept of binary classification is discussed, emphasizing the use of softmax output and cross entropy loss for predicting 0 or 1 outputs', 'The use of mean squared error loss is introduced for optimizing over real number outputs, involving computing the squared error and taking the mean over the entire population', "The empirical loss is the mean of all the losses for individual students, reflecting the model's performance across the entire population of students", 'The step-by-step process of computing the gradient through backpropagation for a simple network with one input, one hidden layer, one hidden unit, and one output is detailed', 'The process of back-propagating gradients through the network contributes to network optimization', 'Stochastic gradient descent may only optimize a local minimum, with no guarantee of reaching the global minimum', 'Extensions of stochastic gradient descent take an adaptive approach to potentially overcome limitations', 'Stochastic gradient descent is widely used due to its cost-effectiveness in computation', 'Batching data for gradient descent allows for a more accurate estimate of the true gradient', 'Dropout is a popular regularization technique in deep learning, improving generalization to unseen test data', 'The validation set loss starts to increase as the training set loss continues to decrease, indicating overfitting', 'Overfitting is a fundamental problem in machine learning, leading to high generalization error when the model is too complex', 'Early stopping involves training the neural network without dropout, preventing the model from becoming too complex to generalize well', 'The training loss always decreases, potentially leading to overfitting if training continues for too long']}