title

MIT 6.S191 (2021): Introduction to Deep Learning

description

MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
For all lectures, slides, and lab materials: http://introtodeeplearning.com/
Lecture Outline
0:00 - Introduction
4:48 - Course information
10:18 - Why deep learning?
12:28 - The perceptron
14:42 - Activation functions
17:48 - Perceptron example
21:43 - From perceptrons to neural networks
27:42 - Applying neural networks
30:21 - Loss functions
33:23 - Training and gradient descent
38:05 - Backpropagation
43:06 - Setting the learning rate
47:17 - Batched gradient descent
49:49 - Regularization: dropout and early stopping
55:55 - Summary
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detail

{'title': 'MIT 6.S191 (2021): Introduction to Deep Learning', 'heatmap': [{'end': 1155.188, 'start': 1118.469, 'weight': 0.776}, {'end': 1539.15, 'start': 1457.537, 'weight': 0.887}, {'end': 1973.609, 'start': 1869.656, 'weight': 0.798}], 'summary': "Mit 6.s191 introduces deep learning's significance, application in speech and video creation, advancements in generating dynamic videos from static images, ethical considerations, and logistics. it also covers neural networks, dense layers, training, and optimization with tensorflow, emphasizing overfitting prevention and model generalization through regularization techniques.", 'chapters': [{'end': 620.989, 'segs': [{'end': 40.414, 'src': 'embed', 'start': 10.362, 'weight': 1, 'content': [{'end': 16.227, 'text': 'Good afternoon, everyone, and welcome to MIT 6S191, Introduction to Deep Learning.', 'start': 10.362, 'duration': 5.865}, {'end': 23.955, 'text': "My name is Alexander Amini, and I'm so excited to be your instructor this year along with Aviso Imani in this new virtual format.", 'start': 17.108, 'duration': 6.847}, {'end': 28.526, 'text': '6S191 is a two-week boot camp on everything deep learning.', 'start': 25.123, 'duration': 3.403}, {'end': 34.43, 'text': "We'll cover a ton of material in only two weeks, so I think it's really important for us to dive right in with these lectures.", 'start': 28.846, 'duration': 5.584}, {'end': 40.414, 'text': 'But before we do that, I do want to motivate exactly why I think this is such an awesome field to study.', 'start': 35.05, 'duration': 5.364}], 'summary': 'Mit 6s191 is a two-week boot camp on deep learning, covering a ton of material in a new virtual format.', 'duration': 30.052, 'max_score': 10.362, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM10362.jpg'}, {'end': 108.711, 'src': 'embed', 'start': 73.288, 'weight': 2, 'content': [{'end': 83.413, 'text': 'Deep learning is revolutionizing so many fields, from robotics to medicine and everything in between.', 'start': 73.288, 'duration': 10.125}, {'end': 92.538, 'text': "You'll learn the fundamentals of this field and how you can build some of these incredible algorithms.", 'start': 84.694, 'duration': 7.844}, {'end': 104.747, 'text': 'In fact, this entire speech and video are not real and were created using deep learning and artificial intelligence.', 'start': 93.914, 'duration': 10.833}, {'end': 108.711, 'text': "And in this class, you'll learn how.", 'start': 106.348, 'duration': 2.363}], 'summary': 'Deep learning revolutionizes fields, creating realistic content using ai.', 'duration': 35.423, 'max_score': 73.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM73288.jpg'}, {'end': 228.477, 'src': 'embed', 'start': 198.897, 'weight': 0, 'content': [{'end': 207.065, 'text': "now it's actually possible to use just a single static image, not the full video, to achieve the exact same thing.", 'start': 198.897, 'duration': 8.168}, {'end': 215.893, 'text': 'and now you can actually see eight more examples of Obama now just created using just a single static image.', 'start': 207.065, 'duration': 8.828}, {'end': 223.4, 'text': 'no more full dynamic videos, but we can achieve the same incredible realism and result using deep learning.', 'start': 215.893, 'duration': 7.507}, {'end': 228.477, 'text': 'now, of course, There is nothing restricting us to one person.', 'start': 223.4, 'duration': 5.077}], 'summary': 'Deep learning can create realistic videos from a single static image, expanding beyond one person.', 'duration': 29.58, 'max_score': 198.897, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM198897.jpg'}, {'end': 383.96, 'src': 'embed', 'start': 357.684, 'weight': 3, 'content': [{'end': 362.547, 'text': 'And we want to provide you with a solid foundation, both technically and practically,', 'start': 357.684, 'duration': 4.863}, {'end': 368.19, 'text': 'for you to understand under the hood how these algorithms are built and how they can learn.', 'start': 362.547, 'duration': 5.643}, {'end': 375.872, 'text': 'So this course is split between technical lectures as well as project software labs.', 'start': 370.947, 'duration': 4.925}, {'end': 383.96, 'text': "We'll cover the foundation starting today with neural networks, which are really the building blocks of everything that we'll see in this course.", 'start': 376.372, 'duration': 7.588}], 'summary': 'Course provides a foundation in neural networks for understanding algorithms and practical application.', 'duration': 26.276, 'max_score': 357.684, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM357684.jpg'}, {'end': 441.078, 'src': 'embed', 'start': 419.752, 'weight': 4, 'content': [{'end': 430.014, 'text': 'The first option will be to actually work in teams of up to four or individually to develop a cool new deep learning idea.', 'start': 419.752, 'duration': 10.262}, {'end': 435.796, 'text': 'Now, doing so will make you eligible to win some of the prizes that you can see on the right-hand side.', 'start': 430.034, 'duration': 5.762}, {'end': 441.078, 'text': 'And we realize that in the context of this class, which is only two weeks,', 'start': 436.796, 'duration': 4.282}], 'summary': 'Develop deep learning idea in teams of up to four for prizes in two-week class.', 'duration': 21.326, 'max_score': 419.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM419752.jpg'}], 'start': 10.362, 'title': 'Mit 6s191: introduction to deep learning', 'summary': 'Introduces mit 6s191, a two-week boot camp on deep learning, emphasizing its significance in revolutionizing various fields and its use of deep learning and artificial intelligence to create speech and video. it also discusses the advancements in deep learning at mit, including the ability to generate realistic dynamic videos from a single static image, the technical and ethical aspects of deep learning, and the logistics of the course including project options and prizes.', 'chapters': [{'end': 108.711, 'start': 10.362, 'title': 'Mit 6s191: introduction to deep learning', 'summary': 'Introduces mit 6s191, a two-week boot camp on deep learning, emphasizing its significance in revolutionizing various fields and its use of deep learning and artificial intelligence to create speech and video.', 'duration': 98.349, 'highlights': ['The chapter introduces MIT 6S191, a two-week boot camp on everything deep learning, emphasizing its significance in revolutionizing various fields. (Relevance: 5)', 'It highlights the use of deep learning and artificial intelligence to create the entire speech and video for the class. (Relevance: 4)', 'Alexander Amini and Aviso Imani are the instructors for MIT 6S191 this year in a new virtual format. (Relevance: 3)', 'Deep learning is revolutionizing various fields from robotics to medicine and everything in between. (Relevance: 2)', 'The class focuses on teaching the fundamentals of deep learning and how to build incredible algorithms using it. (Relevance: 1)']}, {'end': 620.989, 'start': 109.852, 'title': 'Advancements in deep learning at mit', 'summary': 'Discusses the advancements in deep learning at mit, including the ability to generate realistic dynamic videos from a single static image, the technical and ethical aspects of deep learning, and the logistics of the course including project options and prizes.', 'duration': 511.137, 'highlights': ['The ability to generate realistic dynamic videos from a single static image using deep learning has significantly advanced within the past year, showcasing the rapid progress in the field (e.g., from requiring a full video of Obama speaking to using just a single static image).', 'The course aims to provide a solid foundation, both technically and practically, for understanding how deep learning algorithms are built and how they can learn tasks directly from raw data, including technical lectures, project software labs, and new hot topic lectures on uncertainty and probabilistic deep learning, as well as algorithmic bias and fairness.', 'Students have two options to fulfill credit requirements: working in teams or individually to develop a deep learning idea for a project competition, or writing a one-page review on a deep learning paper, with opportunities to win prizes for both options.', 'The logistics of the course include a three-minute project presentation to judges and a one-page review on a deep learning paper, with emphasis on the novelty of the idea and the clarity of writing and technical communication, and the availability of TAs and teaching assistants for support and assistance throughout the course.']}], 'duration': 610.627, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM10362.jpg', 'highlights': ['The ability to generate realistic dynamic videos from a single static image using deep learning has significantly advanced within the past year, showcasing the rapid progress in the field (e.g., from requiring a full video of Obama speaking to using just a single static image).', 'The chapter introduces MIT 6S191, a two-week boot camp on everything deep learning, emphasizing its significance in revolutionizing various fields.', 'It highlights the use of deep learning and artificial intelligence to create the entire speech and video for the class.', 'The course aims to provide a solid foundation, both technically and practically, for understanding how deep learning algorithms are built and how they can learn tasks directly from raw data, including technical lectures, project software labs, and new hot topic lectures on uncertainty and probabilistic deep learning, as well as algorithmic bias and fairness.', 'Students have two options to fulfill credit requirements: working in teams or individually to develop a deep learning idea for a project competition, or writing a one-page review on a deep learning paper, with opportunities to win prizes for both options.']}, {'end': 1276.18, 'segs': [{'end': 746.956, 'src': 'embed', 'start': 672.758, 'weight': 0, 'content': [{'end': 679.902, 'text': 'composing these edges together to detect mid-level features such as an eye or a nose or a mouth,', 'start': 672.758, 'duration': 7.144}, {'end': 687.166, 'text': 'and then going deeper and composing these features into structural facial features so that we can recognize this face?', 'start': 679.902, 'duration': 7.264}, {'end': 695.638, 'text': "This hierarchical way of thinking is really core to deep learning, and it's core to everything that we're going to learn in this class.", 'start': 689.333, 'duration': 6.305}, {'end': 704.976, 'text': 'Actually, the fundamental building blocks, though, of deep learning and neural networks have actually existed for decades.', 'start': 697.614, 'duration': 7.362}, {'end': 713.577, 'text': 'So one interesting thing to consider is why are we studying this now? Now is an incredibly amazing time to study these algorithms.', 'start': 705.396, 'duration': 8.181}, {'end': 718.338, 'text': 'And for one reason, it is because data has become much more pervasive.', 'start': 713.997, 'duration': 4.341}, {'end': 721.559, 'text': 'These models are extremely hungry for data.', 'start': 718.638, 'duration': 2.921}, {'end': 726.5, 'text': "And at the moment, we're living in an era where we have more data than ever before.", 'start': 722.039, 'duration': 4.461}, {'end': 730.884, 'text': 'Secondly, these algorithms are massively parallelizable,', 'start': 727.702, 'duration': 3.182}, {'end': 738.09, 'text': 'so they can benefit tremendously from modern GPU hardware that simply did not exist when these algorithms were developed.', 'start': 730.884, 'duration': 7.206}, {'end': 746.956, 'text': 'And finally, due to open source toolboxes like TensorFlow, building and deploying these models has become extremely streamlined.', 'start': 739.19, 'duration': 7.766}], 'summary': 'Deep learning benefits from abundant data and modern hardware, making it a prime time to study these algorithms.', 'duration': 74.198, 'max_score': 672.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM672758.jpg'}, {'end': 799.719, 'src': 'embed', 'start': 774.278, 'weight': 5, 'content': [{'end': 782.607, 'text': "The idea of a perceptron, or a single neuron, is actually very simple, so I think it's really important for all of you to understand this at its core.", 'start': 774.278, 'duration': 8.329}, {'end': 789.454, 'text': "Let's start by actually talking about the forward propagation of information through this single neuron.", 'start': 783.528, 'duration': 5.926}, {'end': 799.719, 'text': 'We can define a set of inputs xi through xm, which you can see on the left-hand side, and each of these inputs, or each of these numbers,', 'start': 790.194, 'duration': 9.525}], 'summary': 'Understanding the simple concept of a perceptron and its forward propagation is important for all.', 'duration': 25.441, 'max_score': 774.278, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM774278.jpg'}, {'end': 1022.828, 'src': 'embed', 'start': 995.832, 'weight': 2, 'content': [{'end': 1000.035, 'text': 'because often these are the questions that can lead to really amazing research breakthroughs.', 'start': 995.832, 'duration': 4.203}, {'end': 1002.858, 'text': 'So why do we need activation functions?', 'start': 1000.616, 'duration': 2.242}, {'end': 1010.564, 'text': 'Now, the point of an activation function is to actually introduce nonlinearities into our network, because these are nonlinear functions.', 'start': 1003.378, 'duration': 7.186}, {'end': 1015.306, 'text': 'And it allows us to actually deal with non-linear data.', 'start': 1011.545, 'duration': 3.761}, {'end': 1022.828, 'text': 'This is extremely important in real life, especially because in the real world, data is almost always non-linear.', 'start': 1015.806, 'duration': 7.022}], 'summary': 'Activation functions introduce nonlinearities for dealing with real-world non-linear data.', 'duration': 26.996, 'max_score': 995.832, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM995832.jpg'}, {'end': 1155.188, 'src': 'heatmap', 'start': 1118.469, 'weight': 0.776, 'content': [{'end': 1123.21, 'text': 'We take a dot product of x and w, we add our bias, and apply our nonlinearity.', 'start': 1118.469, 'duration': 4.741}, {'end': 1129.552, 'text': "What about what's inside of this nonlinearity g? Well, this is just a 2D line.", 'start': 1123.49, 'duration': 6.062}, {'end': 1135.62, 'text': "In fact, since it's just a two-dimensional line, we can even plot it in two-dimensional space.", 'start': 1130.953, 'duration': 4.667}, {'end': 1137.983, 'text': 'This is called the feature space, the input space.', 'start': 1135.66, 'duration': 2.323}, {'end': 1142.51, 'text': 'In this case, the feature space and the input space are equal because we only have one neuron.', 'start': 1138.304, 'duration': 4.206}, {'end': 1147.004, 'text': "So in this plot, let me describe what you're seeing.", 'start': 1144.082, 'duration': 2.922}, {'end': 1149.745, 'text': "So on the two axes, you're seeing our two inputs.", 'start': 1147.044, 'duration': 2.701}, {'end': 1152.967, 'text': 'So on one axis is x1, one of the inputs.', 'start': 1150.325, 'duration': 2.642}, {'end': 1155.188, 'text': 'On the other axis is x2, our other input.', 'start': 1153.007, 'duration': 2.181}], 'summary': 'Describing feature and input space in a 2d plot.', 'duration': 36.719, 'max_score': 1118.469, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1118469.jpg'}], 'start': 621.89, 'title': 'The significance of deep learning and neural networks', 'summary': 'Delves into the current relevance and advantages of deep learning, highlighting its distinction from traditional machine learning, hierarchical feature learning, and the favorable conditions such as data abundance, parallelizability, and streamlined deployment. it also discusses perceptrons, activation functions, and the capability of neural networks to handle non-linear data and approximate complex functions.', 'chapters': [{'end': 746.956, 'start': 621.89, 'title': 'Deep learning: why now?', 'summary': "Explores the importance of deep learning and why it's relevant now, emphasizing its distinction from traditional machine learning, the hierarchical feature learning process, and the current advantageous conditions including the abundance of data, parallelizability, and streamlined deployment.", 'duration': 125.066, 'highlights': ["The importance of deep learning and why it's relevant now", 'Hierarchical feature learning process', 'Advantageous conditions including the abundance of data, parallelizability, and streamlined deployment']}, {'end': 1276.18, 'start': 750.069, 'title': 'Understanding perceptrons and activation functions', 'summary': 'Explains the concept of perceptrons, forward propagation, nonlinear activation functions, and the necessity of activation functions in neural networks, emphasizing the importance of handling non-linear data and the power of neural networks to approximate complex functions.', 'duration': 526.111, 'highlights': ['The importance of activation functions in handling non-linear data and approximating complex functions in neural networks.', 'Explanation of the forward propagation of information through a single neuron, including the role of inputs, weights, bias, and nonlinear activation functions.', 'Clarification on the necessity of activation functions to introduce nonlinearities into the network and handle non-linear data.']}], 'duration': 654.29, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM621890.jpg', 'highlights': ['Hierarchical feature learning process', 'Advantageous conditions including data abundance, parallelizability, and streamlined deployment', 'The importance of activation functions in handling non-linear data and approximating complex functions in neural networks', 'The necessity of activation functions to introduce nonlinearities into the network and handle non-linear data', "The importance of deep learning and why it's relevant now", 'Explanation of the forward propagation of information through a single neuron, including the role of inputs, weights, bias, and nonlinear activation functions']}, {'end': 1660.35, 'segs': [{'end': 1297.5, 'src': 'embed', 'start': 1276.18, 'weight': 0, 'content': [{'end': 1288.691, 'text': 'for example an input right here we can see exactly that this point is going to be having an activation function less than zero and its output will be less than 0.5..', 'start': 1276.18, 'duration': 12.511}, {'end': 1293.236, 'text': 'The magnitude of that actually is computed by plugging it into the perceptron equation.', 'start': 1288.691, 'duration': 4.545}, {'end': 1297.5, 'text': "So we can't avoid that, but we can immediately get an answer on the decision boundary,", 'start': 1293.256, 'duration': 4.244}], 'summary': 'Input leads to activation < 0, output < 0.5, decision boundary computed.', 'duration': 21.32, 'max_score': 1276.18, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1276180.jpg'}, {'end': 1341.759, 'src': 'embed', 'start': 1318.132, 'weight': 3, 'content': [{'end': 1324.977, 'text': "If there's only a few things that you get from this class, I really want everyone to take away how a perceptron works.", 'start': 1318.132, 'duration': 6.845}, {'end': 1327.479, 'text': "And there's three steps, remember them always.", 'start': 1325.438, 'duration': 2.041}, {'end': 1336.047, 'text': 'The dot product, you take a dot product of your inputs and your weights, you add a bias, and you apply your non-linearity.', 'start': 1327.84, 'duration': 8.207}, {'end': 1337.368, 'text': "There's three steps.", 'start': 1336.587, 'duration': 0.781}, {'end': 1341.759, 'text': "Let's simplify this diagram a little bit.", 'start': 1339.517, 'duration': 2.242}], 'summary': 'Understand the three steps of a perceptron: dot product, bias addition, and non-linearity application.', 'duration': 23.627, 'max_score': 1318.132, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1318132.jpg'}, {'end': 1395.275, 'src': 'embed', 'start': 1363.1, 'weight': 1, 'content': [{'end': 1370.266, 'text': 'The final output though is simply y, which is equal to the activation function of z, which is our activation value.', 'start': 1363.1, 'duration': 7.166}, {'end': 1379.36, 'text': 'Now, if we want to define a multi-output neural network, we can simply add another perceptron to this picture.', 'start': 1372.734, 'duration': 6.626}, {'end': 1383.965, 'text': 'So instead of having one perceptron, now we have two perceptrons and two outputs.', 'start': 1379.941, 'duration': 4.024}, {'end': 1395.275, 'text': 'Each one is a normal perceptron, exactly like we saw before taking its inputs from each of the X1s through XMs, taking the dot product, adding a bias,', 'start': 1384.465, 'duration': 10.81}], 'summary': 'Defining a multi-output neural network with two perceptrons and two outputs.', 'duration': 32.175, 'max_score': 1363.1, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1363100.jpg'}, {'end': 1456.477, 'src': 'embed', 'start': 1433.445, 'weight': 6, 'content': [{'end': 1448.314, 'text': "So, now that we have the understanding of how a single perceptron works and how a dense layer works this is a stack of perceptrons let's try and see how we can actually build up a dense layer like this all the way from scratch.", 'start': 1433.445, 'duration': 14.869}, {'end': 1456.477, 'text': 'To do that, we can actually start by initializing the two components of our dense layer, which are the weights and the biases.', 'start': 1449.275, 'duration': 7.202}], 'summary': 'Exploring building a dense layer from scratch with weights and biases.', 'duration': 23.032, 'max_score': 1433.445, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1433445.jpg'}, {'end': 1539.15, 'src': 'heatmap', 'start': 1457.537, 'weight': 0.887, 'content': [{'end': 1465.339, 'text': 'Now that we have these two parameters of our neural network, of our dense layer, we can actually define the forward propagation of information,', 'start': 1457.537, 'duration': 7.802}, {'end': 1467.599, 'text': 'just like we saw it and learned about already.', 'start': 1465.339, 'duration': 2.26}, {'end': 1475.041, 'text': 'That forward propagation of information is simply the dot product or the matrix multiplication of our inputs with our weights.', 'start': 1468.339, 'duration': 6.702}, {'end': 1480.401, 'text': 'Add a bias that gives us our activation function here.', 'start': 1476.658, 'duration': 3.743}, {'end': 1485.284, 'text': 'And then we apply this non-linearity to compute the output.', 'start': 1481.341, 'duration': 3.943}, {'end': 1491.969, 'text': 'Now, TensorFlow has actually implemented this dense layer for us.', 'start': 1488.146, 'duration': 3.823}, {'end': 1494.531, 'text': "So we don't need to do that from scratch.", 'start': 1492.289, 'duration': 2.242}, {'end': 1496.953, 'text': 'Instead, we can just call it like shown here.', 'start': 1494.951, 'duration': 2.002}, {'end': 1501.296, 'text': 'So to create a dense layer with two outputs, we can specify this units equal to two.', 'start': 1497.053, 'duration': 4.243}, {'end': 1507.77, 'text': "Now let's take a look at what's called a single layered neural network.", 'start': 1504.028, 'duration': 3.742}, {'end': 1512.852, 'text': 'This is when we have a single hidden layer between our inputs and our outputs.', 'start': 1508.25, 'duration': 4.602}, {'end': 1522.176, 'text': 'This layer is called the hidden layer because unlike an input layer and an output layer, the states of this hidden layer are typically unobserved.', 'start': 1513.732, 'duration': 8.444}, {'end': 1523.837, 'text': "They're hidden to some extent.", 'start': 1522.516, 'duration': 1.321}, {'end': 1525.958, 'text': "They're not strictly enforced either.", 'start': 1524.117, 'duration': 1.841}, {'end': 1533.645, 'text': 'And since we have this transformation now from the input layer to the hidden layer and from the hidden layer to the output layer,', 'start': 1526.678, 'duration': 6.967}, {'end': 1539.15, 'text': 'each of these layers are going to have their own specified weight matrices.', 'start': 1533.645, 'duration': 5.505}], 'summary': "Defining forward propagation using tensorflow's implemented dense layer with two outputs and exploring single layered neural network.", 'duration': 81.613, 'max_score': 1457.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1457537.jpg'}, {'end': 1637.414, 'src': 'embed', 'start': 1606.872, 'weight': 2, 'content': [{'end': 1610.394, 'text': 'again using TensorFlow with the predefined dense layer notation.', 'start': 1606.872, 'duration': 3.522}, {'end': 1614.837, 'text': "Here we're creating a sequential model where we can stack layers on top of each other.", 'start': 1611.055, 'duration': 3.782}, {'end': 1621.1, 'text': 'First layer with n neurons and the second layer with two neurons, the output layer.', 'start': 1615.697, 'duration': 5.403}, {'end': 1632.488, 'text': 'And if we want to create a deep neural network, all we have to do is keep stacking these layers to create more and more hierarchical models,', 'start': 1624.178, 'duration': 8.31}, {'end': 1637.414, 'text': 'ones where the final output is computed by going deeper and deeper into the network.', 'start': 1632.488, 'duration': 4.926}], 'summary': 'Using tensorflow to create sequential model with n and 2 neurons, for deep hierarchical networks.', 'duration': 30.542, 'max_score': 1606.872, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1606872.jpg'}], 'start': 1276.18, 'title': 'Building neural networks and dense layers', 'summary': 'Discusses the concept of building neural networks, emphasizing the three key steps of a perceptron, understanding dense layers, creating neural networks using tensorflow, and the importance of understanding the decision boundary in relation to the hyperplane.', 'chapters': [{'end': 1361.5, 'start': 1276.18, 'title': 'Building neural networks', 'summary': 'Discusses the concept of building neural networks, emphasizing the three key steps of a perceptron and the importance of understanding the decision boundary in relation to the hyperplane.', 'duration': 85.32, 'highlights': ['Understanding the three key steps of a perceptron: the dot product of inputs and weights, adding a bias, and applying a non-linearity function, is crucial in building neural networks.', 'The importance of understanding the decision boundary in relation to the hyperplane for immediate determination of output is emphasized.', "The computation of the activation function output being less than 0.5 is highlighted as a key point in the explanation of the perceptron's functionality."]}, {'end': 1660.35, 'start': 1363.1, 'title': 'Building neural networks and dense layers', 'summary': 'Discusses building a multi-output neural network, understanding dense layers, and creating neural networks using tensorflow, emphasizing the concept of dense layers and their implementation in tensorflow.', 'duration': 297.25, 'highlights': ['The chapter explains the concept of multi-output neural networks, where adding another perceptron leads to two outputs.', 'It discusses the implementation of dense layers, emphasizing that each perceptron in the layer will have a different set of weights.', 'The transcript highlights the process of building a dense layer from scratch by initializing the weights and biases and defining the forward propagation of information.', 'It covers the implementation of single layered neural networks and the creation of hierarchical models using TensorFlow by stacking dense layers.', 'The transcript emphasizes the use of TensorFlow to create neural networks, stack layers, and implement deep neural networks by stacking dense layers.']}], 'duration': 384.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1276180.jpg', 'highlights': ['The importance of understanding the decision boundary in relation to the hyperplane for immediate determination of output is emphasized.', 'The chapter explains the concept of multi-output neural networks, where adding another perceptron leads to two outputs.', 'The transcript emphasizes the use of TensorFlow to create neural networks, stack layers, and implement deep neural networks by stacking dense layers.', 'Understanding the three key steps of a perceptron: the dot product of inputs and weights, adding a bias, and applying a non-linearity function, is crucial in building neural networks.', "The computation of the activation function output being less than 0.5 is highlighted as a key point in the explanation of the perceptron's functionality.", 'It covers the implementation of single layered neural networks and the creation of hierarchical models using TensorFlow by stacking dense layers.', 'It discusses the implementation of dense layers, emphasizing that each perceptron in the layer will have a different set of weights.', 'The transcript highlights the process of building a dense layer from scratch by initializing the weights and biases and defining the forward propagation of information.']}, {'end': 2002.548, 'segs': [{'end': 1690.91, 'src': 'embed', 'start': 1660.35, 'weight': 4, 'content': [{'end': 1661.671, 'text': "if that's how many outputs we have.", 'start': 1660.35, 'duration': 1.321}, {'end': 1665.32, 'text': "Okay, so that's awesome.", 'start': 1664.079, 'duration': 1.241}, {'end': 1674.809, 'text': 'Now we have an idea of not only how to build up a neural network directly from a perceptron, but how to compose them together to form complex,', 'start': 1665.52, 'duration': 9.289}, {'end': 1675.81, 'text': 'deep neural networks.', 'start': 1674.809, 'duration': 1.001}, {'end': 1685.459, 'text': "Let's take a look at how we can actually apply them to a very real problem that I believe all of you should care very deeply about.", 'start': 1676.51, 'duration': 8.949}, {'end': 1690.91, 'text': "Here's a problem that we want to build an AI system to learn to answer.", 'start': 1686.988, 'duration': 3.922}], 'summary': 'Learn to build neural networks and apply to real-world problems', 'duration': 30.56, 'max_score': 1660.35, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1660350.jpg'}, {'end': 1777.634, 'src': 'embed', 'start': 1753.461, 'weight': 0, 'content': [{'end': 1761.346, 'text': 'Given everyone else in this class, will you pass or fail this class based on the training data that you see?', 'start': 1753.461, 'duration': 7.885}, {'end': 1762.647, 'text': "So let's do it.", 'start': 1762.046, 'duration': 0.601}, {'end': 1767.149, 'text': 'We have now all of the requirements to do this now.', 'start': 1762.687, 'duration': 4.462}, {'end': 1775.033, 'text': "So let's build a neural network with two inputs, x1 and x2, with x1 being the number of lectures that we attend.", 'start': 1767.229, 'duration': 7.804}, {'end': 1777.634, 'text': 'x2 is the number of hours you spend on your final project.', 'start': 1775.033, 'duration': 2.601}], 'summary': 'Building a neural network with two inputs: lectures attended and hours spent on final project.', 'duration': 24.173, 'max_score': 1753.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1753461.jpg'}, {'end': 1973.609, 'src': 'heatmap', 'start': 1869.656, 'weight': 0.798, 'content': [{'end': 1876.639, 'text': "Now, let's assume we have not just the data from one student, but as we have in this case, the data from many students.", 'start': 1869.656, 'duration': 6.983}, {'end': 1885.924, 'text': 'We now care about not just how the model did on predicting just one prediction, but how it did on average across all of these students.', 'start': 1877.16, 'duration': 8.764}, {'end': 1889.146, 'text': 'This is what we call the empirical loss,', 'start': 1886.444, 'duration': 2.702}, {'end': 1895.889, 'text': "and it's simply just the mean or the average of every loss from each individual example or each individual student.", 'start': 1889.146, 'duration': 6.743}, {'end': 1907.644, 'text': 'When training a neural network, we want to find a network that minimizes the empirical loss between our predictions and the true outputs.', 'start': 1898.339, 'duration': 9.305}, {'end': 1917.89, 'text': 'Now, if we look at the problem of binary classification, where the neural network, like we want to do in this case,', 'start': 1910.506, 'duration': 7.384}, {'end': 1925.575, 'text': 'is supposed to answer either yes or no, one or zero, we can use what is called a softmax cross-entropy loss.', 'start': 1917.89, 'duration': 7.685}, {'end': 1938.874, 'text': "Now the softmax cross entropy loss is actually written out here and it's defined by actually what's called the cross entropy between two probability distributions.", 'start': 1926.67, 'duration': 12.204}, {'end': 1946.276, 'text': 'It measures how far apart the ground truth probability distribution is from the predicted probability distribution.', 'start': 1938.934, 'duration': 7.342}, {'end': 1955.632, 'text': "Let's suppose, instead of predicting binary outputs, will I pass this class or will I not pass this class?", 'start': 1948.868, 'duration': 6.764}, {'end': 1963.162, 'text': 'instead, you want to predict the final grade as a real number, not a probability or as a percentage.', 'start': 1955.632, 'duration': 7.53}, {'end': 1968.846, 'text': 'We want the grade that you will get in this class.', 'start': 1963.502, 'duration': 5.344}, {'end': 1973.609, 'text': 'Now in this case, because the type of the output is different.', 'start': 1969.646, 'duration': 3.963}], 'summary': 'Neural network minimizes empirical loss in binary classification using softmax cross-entropy loss.', 'duration': 103.953, 'max_score': 1869.656, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1869656.jpg'}, {'end': 1925.575, 'src': 'embed', 'start': 1877.16, 'weight': 2, 'content': [{'end': 1885.924, 'text': 'We now care about not just how the model did on predicting just one prediction, but how it did on average across all of these students.', 'start': 1877.16, 'duration': 8.764}, {'end': 1889.146, 'text': 'This is what we call the empirical loss,', 'start': 1886.444, 'duration': 2.702}, {'end': 1895.889, 'text': "and it's simply just the mean or the average of every loss from each individual example or each individual student.", 'start': 1889.146, 'duration': 6.743}, {'end': 1907.644, 'text': 'When training a neural network, we want to find a network that minimizes the empirical loss between our predictions and the true outputs.', 'start': 1898.339, 'duration': 9.305}, {'end': 1917.89, 'text': 'Now, if we look at the problem of binary classification, where the neural network, like we want to do in this case,', 'start': 1910.506, 'duration': 7.384}, {'end': 1925.575, 'text': 'is supposed to answer either yes or no, one or zero, we can use what is called a softmax cross-entropy loss.', 'start': 1917.89, 'duration': 7.685}], 'summary': 'We aim to minimize the empirical loss in training neural networks for binary classification, using softmax cross-entropy loss.', 'duration': 48.415, 'max_score': 1877.16, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1877160.jpg'}], 'start': 1660.35, 'title': 'Neural networks', 'summary': 'Covers building a neural network to predict student success based on lectures attended and project hours, and emphasizes the importance of training, minimizing loss, and using different functions for classification and regression.', 'chapters': [{'end': 1777.634, 'start': 1660.35, 'title': 'Neural networks for decision making', 'summary': "Explains the process of building a neural network to predict a student's success in a class based on the number of lectures attended and hours spent on a final project, using past training data from success 191.", 'duration': 117.284, 'highlights': ['A neural network is built from a perceptron, composing them to form complex, deep neural networks.', "Applying the neural network to predict a student's success in a class based on the number of lectures attended and hours spent on a final project, using past training data from Success 191.", 'Training data from past participants of Success 191 consists of green points indicating students who passed the class and red points indicating students who failed.', "Building a neural network with two inputs, x1 and x2, representing the number of lectures attended and hours spent on the final project, to determine the student's likelihood of passing the class."]}, {'end': 2002.548, 'start': 1778.234, 'title': 'Neural network loss and training', 'summary': 'Explains the importance of training neural networks, minimizing loss, and using different loss functions for binary classification and regression, emphasizing the need for model training and interpretation.', 'duration': 224.314, 'highlights': ['The loss of a neural network defines how wrong a prediction was, with a larger loss indicating a significant difference between predicted and ground truth outputs.', 'The empirical loss, determined by averaging the individual losses across multiple examples, is crucial for training a neural network to minimize the difference between predictions and true outputs.', 'Different loss functions are used for binary classification (softmax cross-entropy loss) and regression (mean squared error), addressing the need for specific loss functions based on the type of output (binary or continuous).']}], 'duration': 342.198, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM1660350.jpg', 'highlights': ["Applying the neural network to predict a student's success in a class based on the number of lectures attended and hours spent on a final project, using past training data from Success 191.", "Building a neural network with two inputs, x1 and x2, representing the number of lectures attended and hours spent on the final project, to determine the student's likelihood of passing the class.", 'The empirical loss, determined by averaging the individual losses across multiple examples, is crucial for training a neural network to minimize the difference between predictions and true outputs.', 'Different loss functions are used for binary classification (softmax cross-entropy loss) and regression (mean squared error), addressing the need for specific loss functions based on the type of output (binary or continuous).', 'A neural network is built from a perceptron, composing them to form complex, deep neural networks.']}, {'end': 2776.584, 'segs': [{'end': 2099.617, 'src': 'embed', 'start': 2026.334, 'weight': 0, 'content': [{'end': 2032.957, 'text': 'How can we use our loss function to train the weights of our neural network such that it can actually learn that problem?', 'start': 2026.334, 'duration': 6.623}, {'end': 2041.251, 'text': 'Well, what we want to do is actually find the weights of the neural network that will minimize the loss of our dataset.', 'start': 2034.585, 'duration': 6.666}, {'end': 2049.438, 'text': "That essentially means that we want to find the W's in our neural network that minimize J.", 'start': 2041.391, 'duration': 8.047}, {'end': 2057.146, 'text': 'J is our empirical cost function that we saw in the previous slides, that average loss over each data point in the dataset.', 'start': 2049.438, 'duration': 7.708}, {'end': 2067.551, 'text': 'Now remember that W capital W is simply a collection of all of the weights in our neural network, not just from one layer,', 'start': 2059.864, 'duration': 7.687}, {'end': 2068.812, 'text': 'but from every single layer.', 'start': 2067.551, 'duration': 1.261}, {'end': 2073.976, 'text': "So that's W zero from the zeroth layer to the first layer to the second layer, all concatenated into one.", 'start': 2068.851, 'duration': 5.125}, {'end': 2080.163, 'text': 'In this optimization problem, we want to optimize all of the Ws to minimize this empirical loss.', 'start': 2074.417, 'duration': 5.746}, {'end': 2086.652, 'text': 'Now remember, our loss function is just a simple function of our weights.', 'start': 2082.351, 'duration': 4.301}, {'end': 2093.935, 'text': 'If we have only two weights, we can actually plot this entire loss landscape over this grid of weights.', 'start': 2087.193, 'duration': 6.742}, {'end': 2099.617, 'text': 'So on the one axis on the bottom, you can see weight number one, and the other one you can see weight zero.', 'start': 2093.955, 'duration': 5.662}], 'summary': 'Train neural network by minimizing loss function with weights optimization.', 'duration': 73.283, 'max_score': 2026.334, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2026334.jpg'}, {'end': 2181.099, 'src': 'embed', 'start': 2153.727, 'weight': 6, 'content': [{'end': 2160.51, 'text': "If we compute the gradient of our loss with respect to our weights, that's the derivative of our gradient for loss with respect to the weights.", 'start': 2153.727, 'duration': 6.783}, {'end': 2165.693, 'text': 'that tells us the direction of which way is up on that lost landscape, from where we stand right now.', 'start': 2160.51, 'duration': 5.183}, {'end': 2170.259, 'text': 'Instead of going up though, we want to find the lowest loss.', 'start': 2167.256, 'duration': 3.003}, {'end': 2176.426, 'text': "So let's take the negative of our gradient and take a small step in that direction.", 'start': 2170.64, 'duration': 5.786}, {'end': 2181.099, 'text': 'Okay, and this will move us a little bit closer to the lowest point.', 'start': 2177.836, 'duration': 3.263}], 'summary': 'Using gradient descent, taking small steps in the negative gradient direction helps minimize loss.', 'duration': 27.372, 'max_score': 2153.727, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2153727.jpg'}, {'end': 2498.757, 'src': 'embed', 'start': 2470.461, 'weight': 4, 'content': [{'end': 2472.383, 'text': "And that's the backpropagation algorithm.", 'start': 2470.461, 'duration': 1.922}, {'end': 2474.464, 'text': 'In theory, it sounds very simple.', 'start': 2472.763, 'duration': 1.701}, {'end': 2482.311, 'text': "It's just a very, very basic extension on derivatives and the chain rule.", 'start': 2475.105, 'duration': 7.206}, {'end': 2494.481, 'text': "But now let's actually touch on some insights from training these networks in practice that make this process much more complicated in practice and why using backpropagation,", 'start': 2482.371, 'duration': 12.11}, {'end': 2496.483, 'text': 'as we saw there, is not always so easy.', 'start': 2494.481, 'duration': 2.002}, {'end': 2498.757, 'text': 'Now in practice,', 'start': 2497.455, 'duration': 1.302}], 'summary': 'Backpropagation seems simple in theory, but practical application is more complex.', 'duration': 28.296, 'max_score': 2470.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2470461.jpg'}, {'end': 2735.989, 'src': 'embed', 'start': 2708.18, 'weight': 5, 'content': [{'end': 2719.303, 'text': 'What if we could say instead how can we build an adaptive learning rate that actually looks at its lost landscape and adapts itself to account for what it sees in the landscape?', 'start': 2708.18, 'duration': 11.123}, {'end': 2723.265, 'text': 'There are actually many types of optimizers that do exactly this.', 'start': 2719.904, 'duration': 3.361}, {'end': 2726.846, 'text': 'This means that the learning rates are no longer fixed.', 'start': 2723.865, 'duration': 2.981}, {'end': 2735.989, 'text': "They can increase or decrease depending on how large the gradient is in that location and how fast we want and how fast we're actually learning,", 'start': 2726.866, 'duration': 9.123}], 'summary': 'Adaptive learning rates adjust based on landscape to improve gradient descent.', 'duration': 27.809, 'max_score': 2708.18, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2708180.jpg'}], 'start': 2005.123, 'title': 'Neural network training and gradient descent', 'summary': 'Discusses training neural networks with loss functions to minimize empirical loss and explains gradient descent, backpropagation, and the significance of adaptive learning rates in optimizing the process.', 'chapters': [{'end': 2128.649, 'start': 2005.123, 'title': 'Neural network training', 'summary': 'Discusses the use of loss functions in training neural networks, emphasizing the optimization of weights to minimize empirical loss to achieve optimal performance.', 'duration': 123.526, 'highlights': ['The process of training a neural network involves optimizing the weights to minimize the empirical loss, which is the average loss over each data point in the dataset.', 'The weights of the neural network are adjusted to minimize the empirical cost function, J, which represents the average loss over each data point in the dataset.', 'The goal is to find the weights that minimize the loss of the dataset, which involves optimizing all the weights in the neural network, including those from every single layer.', 'The loss function is a simple function of the weights, and the optimization process aims to find the lowest point in the loss landscape to determine the optimal weights for the neural network.']}, {'end': 2776.584, 'start': 2129.53, 'title': 'Gradient descent and backpropagation', 'summary': 'Explains the gradient descent algorithm and backpropagation, emphasizing the challenges of training neural networks and the significance of adaptive learning rates in optimizing the process.', 'duration': 647.054, 'highlights': ['The chapter explains the gradient descent algorithm and backpropagation', 'Challenges of training neural networks and the significance of adaptive learning rates', 'The process of computing the gradient and utilizing the negative gradient to approach the lowest loss', 'Importance of adaptive learning rates in optimizing the process']}], 'duration': 771.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2005123.jpg', 'highlights': ['The process of training a neural network involves optimizing the weights to minimize the empirical loss', 'The weights of the neural network are adjusted to minimize the empirical cost function, J', 'The loss function is a simple function of the weights, and the optimization process aims to find the lowest point in the loss landscape', 'The goal is to find the weights that minimize the loss of the dataset, involving optimizing all the weights in the neural network', 'The chapter explains the gradient descent algorithm and backpropagation', 'Importance of adaptive learning rates in optimizing the process', 'The process of computing the gradient and utilizing the negative gradient to approach the lowest loss', 'Challenges of training neural networks and the significance of adaptive learning rates']}, {'end': 3394.691, 'segs': [{'end': 2800.689, 'src': 'embed', 'start': 2777.345, 'weight': 1, 'content': [{'end': 2786.366, 'text': 'So here we can see a full loop of using TensorFlow to define your model on the first line, define your optimizer.', 'start': 2777.345, 'duration': 9.021}, {'end': 2789.787, 'text': 'Here you can replace this with any optimizer that you want.', 'start': 2786.626, 'duration': 3.161}, {'end': 2793.128, 'text': "Here I'm just using stochastic gradient descent like we saw before.", 'start': 2789.947, 'duration': 3.181}, {'end': 2797.069, 'text': 'And feeding it through the model, we loop forever.', 'start': 2794.428, 'duration': 2.641}, {'end': 2799.069, 'text': "We're doing this forward prediction.", 'start': 2797.169, 'duration': 1.9}, {'end': 2800.689, 'text': 'We predict using our model.', 'start': 2799.289, 'duration': 1.4}], 'summary': 'Using tensorflow to define model, optimizer, and predict.', 'duration': 23.344, 'max_score': 2777.345, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2777345.jpg'}, {'end': 2972.24, 'src': 'embed', 'start': 2943.723, 'weight': 2, 'content': [{'end': 2948.306, 'text': "much faster to compute than regular gradient descent, and it's also much,", 'start': 2943.723, 'duration': 4.583}, {'end': 2953.949, 'text': 'much more accurate than purely stochastic gradient descent that only uses a single example.', 'start': 2948.306, 'duration': 5.643}, {'end': 2963.026, 'text': 'Now this increases the gradient accuracy estimation, which also allows us to converge much more smoothly.', 'start': 2956.857, 'duration': 6.169}, {'end': 2968.514, 'text': 'It also means that we can trust our gradient more than in stochastic gradient descent,', 'start': 2964.128, 'duration': 4.386}, {'end': 2972.24, 'text': 'so that we can actually increase our learning rate a bit more as well.', 'start': 2968.514, 'duration': 3.726}], 'summary': 'Accelerated gradient descent is faster and more accurate than regular and stochastic gradient descent, leading to smoother convergence and allowing for higher learning rates.', 'duration': 28.517, 'max_score': 2943.723, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2943723.jpg'}, {'end': 3014.445, 'src': 'embed', 'start': 2993.469, 'weight': 5, 'content': [{'end': 3002.695, 'text': 'This is also known as the problem of generalization and is one of the most fundamental problems in all of machine learning and not just deep learning.', 'start': 2993.469, 'duration': 9.226}, {'end': 3014.445, 'text': "Now, overfitting, like I said, is critical to understand, so I really want to make sure that this is a clear concept in everyone's mind.", 'start': 3005.56, 'duration': 8.885}], 'summary': 'Overfitting is a fundamental problem in machine learning and not just deep learning.', 'duration': 20.976, 'max_score': 2993.469, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2993469.jpg'}, {'end': 3165.405, 'src': 'embed', 'start': 3132.966, 'weight': 3, 'content': [{'end': 3136.989, 'text': 'One very common technique, and very simple to understand, is called dropout.', 'start': 3132.966, 'duration': 4.023}, {'end': 3141.752, 'text': "This is one of the most popular forms of regularization in deep learning, and it's very simple.", 'start': 3137.009, 'duration': 4.743}, {'end': 3145.454, 'text': "Let's revisit this picture of a neural network.", 'start': 3142.472, 'duration': 2.982}, {'end': 3148.857, 'text': 'This is a two-layered neural network, two hidden layers.', 'start': 3145.534, 'duration': 3.323}, {'end': 3158.462, 'text': 'And in dropout, during training, all we simply do is randomly set some of the activations here to zero with some probability.', 'start': 3150.017, 'duration': 8.445}, {'end': 3165.405, 'text': "So what we can do is, let's say we pick our probability to be 50% or 0.5.", 'start': 3159.102, 'duration': 6.303}], 'summary': 'Dropout is a popular regularization technique in deep learning, randomly setting activations to zero with a given probability.', 'duration': 32.439, 'max_score': 3132.966, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM3132966.jpg'}, {'end': 3270.037, 'src': 'embed', 'start': 3242.719, 'weight': 4, 'content': [{'end': 3248.201, 'text': "The second regularization technique that we'll talk about is this notion of early stopping.", 'start': 3242.719, 'duration': 5.482}, {'end': 3251.423, 'text': 'And again here, the idea is very basic.', 'start': 3248.822, 'duration': 2.601}, {'end': 3261.167, 'text': "It's basically let's stop training once we realize that our loss is increasing on a held out validation or let's call it a test set.", 'start': 3251.583, 'duration': 9.584}, {'end': 3270.037, 'text': 'So when we start training, we all know the definition of overfitting is when our model starts to perform worse on the test set.', 'start': 3262.731, 'duration': 7.306}], 'summary': 'Regularization technique: early stopping to prevent overfitting.', 'duration': 27.318, 'max_score': 3242.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM3242719.jpg'}, {'end': 3387.927, 'src': 'embed', 'start': 3367.277, 'weight': 0, 'content': [{'end': 3378.042, 'text': 'We learned about stacking and composing these perceptrons together to form complex hierarchical neural networks and how to mathematically optimize these models with backpropagation.', 'start': 3367.277, 'duration': 10.765}, {'end': 3386.526, 'text': "And finally, we address the practical side of these models that you'll find useful for the labs today, including adaptive learning rates,", 'start': 3379.022, 'duration': 7.504}, {'end': 3387.927, 'text': 'batching and regularization.', 'start': 3386.526, 'duration': 1.401}], 'summary': 'Learned about composing perceptrons, optimizing with backpropagation, and practical aspects for labs today.', 'duration': 20.65, 'max_score': 3367.277, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM3367277.jpg'}], 'start': 2777.345, 'title': 'Implementing neural networks with tensorflow and regularization techniques', 'summary': 'Covers implementing a neural network model using tensorflow, batching data for efficient computation, and addressing overfitting, emphasizing achieving a balanced model capacity. it also discusses regularization techniques like dropout and early stopping to prevent overfitting and improve generalization.', 'chapters': [{'end': 3089.84, 'start': 2777.345, 'title': 'Training neural networks with tensorflow', 'summary': 'Covers the implementation of a neural network model using tensorflow, the concept of batching data for efficient computation, and the issue of overfitting in machine learning, emphasizing the importance of achieving a balanced model capacity for generalization.', 'duration': 312.495, 'highlights': ['The concept of batching data for gradient computation is introduced, with the advantages of faster computation and increased accuracy compared to stochastic gradient descent.', 'The issue of overfitting is discussed, highlighting the importance of striking a balance in model capacity to achieve generalization, with examples of underfitting and overfitting scenarios provided.', 'The implementation of a neural network model using TensorFlow, involving defining the model, selecting an optimizer, and updating weights through gradient application, is outlined.']}, {'end': 3394.691, 'start': 3091.94, 'title': 'Regularization techniques in neural networks', 'summary': 'Discusses regularization techniques, including dropout and early stopping, to prevent overfitting in neural networks, with dropout randomly setting activations to zero with a certain probability and early stopping halting training to prevent overfitting, thus allowing the model to generalize better to unseen data.', 'duration': 302.751, 'highlights': ["Dropout is a popular form of regularization in deep learning, randomly setting some activations to zero with a certain probability, such as 50%, to lower the network's capacity and make it resilient to overfitting.", 'Early stopping is a basic technique that halts training once the validation loss starts increasing, preventing the model from overfitting and ensuring that the model generalizes well to unseen data.', 'The lecture covers fundamental building blocks of neural networks, composing perceptrons to form hierarchical networks, mathematical optimization with backpropagation, and practical techniques like adaptive learning rates, batching, and regularization.']}], 'duration': 617.346, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5tvmMX8r_OM/pics/5tvmMX8r_OM2777345.jpg', 'highlights': ['The lecture covers fundamental building blocks of neural networks, composing perceptrons to form hierarchical networks, mathematical optimization with backpropagation, and practical techniques like adaptive learning rates, batching, and regularization.', 'The implementation of a neural network model using TensorFlow, involving defining the model, selecting an optimizer, and updating weights through gradient application, is outlined.', 'The concept of batching data for gradient computation is introduced, with the advantages of faster computation and increased accuracy compared to stochastic gradient descent.', "Dropout is a popular form of regularization in deep learning, randomly setting some activations to zero with a certain probability, such as 50%, to lower the network's capacity and make it resilient to overfitting.", 'Early stopping is a basic technique that halts training once the validation loss starts increasing, preventing the model from overfitting and ensuring that the model generalizes well to unseen data.', 'The issue of overfitting is discussed, highlighting the importance of striking a balance in model capacity to achieve generalization, with examples of underfitting and overfitting scenarios provided.']}], 'highlights': ['The ability to generate realistic dynamic videos from a single static image using deep learning has significantly advanced within the past year, showcasing the rapid progress in the field (e.g., from requiring a full video of Obama speaking to using just a single static image).', 'The lecture covers fundamental building blocks of neural networks, composing perceptrons to form hierarchical networks, mathematical optimization with backpropagation, and practical techniques like adaptive learning rates, batching, and regularization.', 'The process of training a neural network involves optimizing the weights to minimize the empirical loss', 'The importance of understanding the decision boundary in relation to the hyperplane for immediate determination of output is emphasized.', 'The chapter introduces MIT 6S191, a two-week boot camp on everything deep learning, emphasizing its significance in revolutionizing various fields.']}