title

MIT 6.S191 (2020): Introduction to Deep Learning

description

MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini
January 2020
For all lectures, slides, and lab materials: http://introtodeeplearning.com
Lecture Outline
0:00 - Introduction
4:14 - Course information
8:10 - Why deep learning?
11:01 - The perceptron
13:07 - Activation functions
15:32 - Perceptron example
18:54 - From perceptrons to neural networks
25:23 - Applying neural networks
28:16 - Loss functions
31:14 - Training and gradient descent
35:13 - Backpropagation
39:25 - Setting the learning rate
43:43 - Batched gradient descent
46:46 - Regularization: dropout and early stopping
51:58 - Summary
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detail

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'start': 10.197, 'duration': 0.68}, {'end': 16.4, 'text': 'Good afternoon, and welcome to MIT 6S191.', 'start': 12.198, 'duration': 4.202}, {'end': 19.622, 'text': 'This is really incredible to see the turnout this year.', 'start': 16.42, 'duration': 3.202}, {'end': 25.926, 'text': "This is the fourth year now we're teaching this course, and every single year it just seems to be getting bigger and bigger.", 'start': 19.702, 'duration': 6.224}, {'end': 31.289, 'text': '6S191 is a one-week intensive boot camp on everything deep learning.', 'start': 26.887, 'duration': 4.402}, {'end': 33.239, 'text': 'In the past.', 'start': 32.497, 'duration': 0.742}], 'summary': 'Mit 6s191, a one-week deep learning boot camp, sees increasing turnout every year.', 'duration': 30.307, 'max_score': 2.932, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2932.jpg'}, {'end': 153.796, 'src': 'embed', 'start': 83.095, 'weight': 1, 'content': [{'end': 90.519, 'text': 'is revolutionizing so many fields, from robotics to medicine and everything in between.', 'start': 83.095, 'duration': 7.424}, {'end': 99.704, 'text': "You'll learn the fundamentals of this field and how you can build some of these incredible algorithms.", 'start': 91.8, 'duration': 7.904}, {'end': 111.81, 'text': 'In fact, this entire speech and video are not real and were created using deep learning and artificial intelligence.', 'start': 101.024, 'duration': 10.786}, {'end': 115.83, 'text': "And in this class, you'll learn how.", 'start': 113.447, 'duration': 2.383}, {'end': 121.516, 'text': 'It has been an honor to speak with you today, and I hope you enjoy the course.', 'start': 116.951, 'duration': 4.565}, {'end': 135.689, 'text': 'So as you can tell, deep learning is an incredibly powerful tool.', 'start': 132.068, 'duration': 3.621}, {'end': 145.213, 'text': "This was just an example of how we use deep learning to perform voice synthesis and actually emulate someone else's voice in this case,", 'start': 136.19, 'duration': 9.023}, {'end': 153.796, 'text': 'Barack Obama and also using video dialogue replacement to actually create that video with the help of Kani AI.', 'start': 145.213, 'duration': 8.583}], 'summary': 'Deep learning is revolutionizing fields like robotics and medicine, creating synthetic content using ai and deep learning techniques.', 'duration': 70.701, 'max_score': 83.095, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk83095.jpg'}, {'end': 252.479, 'src': 'embed', 'start': 227.329, 'weight': 2, 'content': [{'end': 233.111, 'text': "And that's what this class is all about teaching algorithms how to learn a task directly from raw data.", 'start': 227.329, 'duration': 5.782}, {'end': 242.135, 'text': 'And we want to provide you with a solid foundation of how you can understand or how to understand these algorithms under the hood,', 'start': 233.912, 'duration': 8.223}, {'end': 250.238, 'text': 'but also provide you with the practical knowledge and practical skills to implement state of the art deep learning algorithms in TensorFlow,', 'start': 242.135, 'duration': 8.103}, {'end': 252.479, 'text': 'which is a very popular deep learning toolbox.', 'start': 250.238, 'duration': 2.241}], 'summary': 'Class teaches algorithms to learn from raw data, covering tensorflow for deep learning.', 'duration': 25.15, 'max_score': 227.329, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk227329.jpg'}, {'end': 340.735, 'src': 'embed', 'start': 309.997, 'weight': 5, 'content': [{'end': 315.099, 'text': 'Also, I should mention that after each day of lectures, so after today, we have two lectures.', 'start': 309.997, 'duration': 5.102}, {'end': 322.742, 'text': "And after each day of lectures, we'll have a software lab, which tries to focus and build upon all of the things that you've learned in that day.", 'start': 315.259, 'duration': 7.483}, {'end': 328.045, 'text': "So you'll get the foundations during the lectures, and you'll get the practical knowledge during the software lab.", 'start': 323.003, 'duration': 5.042}, {'end': 330.966, 'text': 'So the two are kind of jointly coupled in that sense.', 'start': 328.065, 'duration': 2.901}, {'end': 340.735, 'text': 'For those of you taking this class for credit, you have a couple different options to fulfill your credit requirement.', 'start': 335.37, 'duration': 5.365}], 'summary': 'After each day of lectures, there are two lectures and a software lab to reinforce learning.', 'duration': 30.738, 'max_score': 309.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk309997.jpg'}, {'end': 447.269, 'src': 'embed', 'start': 425.39, 'weight': 4, 'content': [{'end': 435.179, 'text': 'So we definitely encourage everyone to enter this competition and have a chance to win these GPUs and these other cool prizes like Google Home and the SSD cards as well.', 'start': 425.39, 'duration': 9.789}, {'end': 441.404, 'text': 'Also, for each of the labs, the three labs will have corresponding prizes.', 'start': 438.021, 'duration': 3.383}, {'end': 447.269, 'text': 'So instructions to actually enter those respective competitions will be within the labs themselves.', 'start': 441.484, 'duration': 5.785}], 'summary': 'Enter the competition to win gpus, google home, and ssd cards, with corresponding prizes for each lab.', 'duration': 21.879, 'max_score': 425.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk425390.jpg'}], 'start': 2.932, 'title': 'Mit 6s191 - deep learning boot camp and introduction to ai and deep learning', 'summary': 'Provides an overview of mit 6s191, a one-week intensive boot camp on deep learning, and introduces concepts of artificial intelligence, machine learning, and deep learning, emphasizing practical implementation and offering project options and prizes for participants.', 'chapters': [{'end': 153.796, 'start': 2.932, 'title': 'Mit 6s191 - deep learning boot camp', 'summary': 'Introduces mit 6s191, a one-week intensive boot camp on deep learning, highlighting its growth over the years and the potential impact of deep learning in various fields, including its practical implementation and the use of ai for voice and video synthesis.', 'duration': 150.864, 'highlights': ['The MIT 6S191 boot camp on deep learning has been taught for the fourth consecutive year, with increasing participation each year.', 'Deep learning is revolutionizing various fields, including robotics and medicine, and the course aims to provide fundamentals and practical knowledge for implementing deep learning algorithms.', 'The speech and video demonstration were created using deep learning and artificial intelligence, showcasing the power of deep learning in voice synthesis and video dialogue replacement.']}, {'end': 489.364, 'start': 155.689, 'title': 'Introduction to artificial intelligence and deep learning', 'summary': 'Introduces the terms intelligence, artificial intelligence, machine learning, and deep learning, outlining the focus on building algorithms to process information and inform future decisions, with a practical emphasis on implementing state-of-the-art deep learning algorithms in tensorflow and offering various project options and prizes for participants.', 'duration': 333.675, 'highlights': ['The class focuses on teaching algorithms how to learn a task directly from raw data, with a practical emphasis on implementing state-of-the-art deep learning algorithms in TensorFlow.', 'Participants have options to propose a project in groups or write a one-page paper review, with prizes including NVIDIA GPUs, Google Home, and SSD cards.', 'The course offers software labs after each day of lectures to build upon the knowledge gained during the lectures.']}], 'duration': 486.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2932.jpg', 'highlights': ['The MIT 6S191 boot camp on deep learning has been taught for the fourth consecutive year, with increasing participation each year.', 'Deep learning is revolutionizing various fields, including robotics and medicine, and the course aims to provide fundamentals and practical knowledge for implementing deep learning algorithms.', 'The class focuses on teaching algorithms how to learn a task directly from raw data, with a practical emphasis on implementing state-of-the-art deep learning algorithms in TensorFlow.', 'The speech and video demonstration were created using deep learning and artificial intelligence, showcasing the power of deep learning in voice synthesis and video dialogue replacement.', 'Participants have options to propose a project in groups or write a one-page paper review, with prizes including NVIDIA GPUs, Google Home, and SSD cards.', 'The course offers software labs after each day of lectures to build upon the knowledge gained during the lectures.']}, {'end': 1526.617, 'segs': [{'end': 670.443, 'src': 'embed', 'start': 583.4, 'weight': 0, 'content': [{'end': 590.725, 'text': 'And then the deep learning algorithm is going to develop some hierarchical representation of first detecting lines and edges in the image,', 'start': 583.4, 'duration': 7.325}, {'end': 597.348, 'text': 'using these lines and edges to detect corners and eyes and mid-level features like eyes, noses, mouths, ears,', 'start': 590.725, 'duration': 6.623}, {'end': 603.552, 'text': 'then composing these together to detect higher level features like maybe jaw lines, side of the face, et cetera,', 'start': 597.348, 'duration': 6.204}, {'end': 606.273, 'text': 'which then can be used to detect the final face structure.', 'start': 603.552, 'duration': 2.721}, {'end': 613.177, 'text': 'And actually, the fundamental building blocks of deep learning have existed for decades.', 'start': 608.714, 'duration': 4.463}, {'end': 619, 'text': 'And their underlying algorithms for training these models have also existed for many years.', 'start': 614.117, 'duration': 4.883}, {'end': 625.031, 'text': 'So why are we studying this now? Well, for one, data has become much more pervasive.', 'start': 619.64, 'duration': 5.391}, {'end': 632.394, 'text': "We're living in the age of big data, and these algorithms are hungry for huge amounts of data to succeed.", 'start': 625.511, 'duration': 6.883}, {'end': 637.776, 'text': 'Secondly, these algorithms are massively parallelizable,', 'start': 634.515, 'duration': 3.261}, {'end': 646.7, 'text': 'which means that they can benefit tremendously from modern GPU architectures and hardware acceleration that simply did not exist when these algorithms were developed.', 'start': 637.776, 'duration': 8.924}, {'end': 654.19, 'text': "And finally, due to open source toolboxes like TensorFlow, which you'll get experience with in this class,", 'start': 648.345, 'duration': 5.845}, {'end': 660.695, 'text': 'building and deploying these models has become extremely streamlined, so much so that we can condense all of this material down into one week.', 'start': 654.19, 'duration': 6.505}, {'end': 670.443, 'text': "So let's start with the fundamental building block of a neural network, which is a single neuron, or what's also called a perceptron.", 'start': 663.017, 'duration': 7.426}], 'summary': 'Deep learning algorithms detect facial features using hierarchical representation, benefiting from big data, parallelization, and modern hardware.', 'duration': 87.043, 'max_score': 583.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk583400.jpg'}, {'end': 890.765, 'src': 'heatmap', 'start': 855.618, 'weight': 0.925, 'content': [{'end': 858.121, 'text': "You'll see these kind of scattered in throughout the slides.", 'start': 855.618, 'duration': 2.503}, {'end': 862.185, 'text': 'No need to really take furious notes at these code blocks.', 'start': 859.762, 'duration': 2.423}, {'end': 864.507, 'text': 'Like I said, all of the slides are published online.', 'start': 862.465, 'duration': 2.042}, {'end': 871.533, 'text': 'So especially during your labs, if you want to refer back to any of the slides, you can always do that from the online lecture notes.', 'start': 864.607, 'duration': 6.926}, {'end': 881.08, 'text': 'Now, why do we care about activation functions? The point of an activation function is to introduce nonlinearities into the data.', 'start': 873.535, 'duration': 7.545}, {'end': 887.783, 'text': 'And this is actually really important in real life, because in real life, almost all of our data is nonlinear.', 'start': 882.281, 'duration': 5.502}, {'end': 890.765, 'text': "And here's a concrete example.", 'start': 889.004, 'duration': 1.761}], 'summary': 'Activation functions introduce nonlinearities to handle real-life data. slides available online for reference.', 'duration': 35.147, 'max_score': 855.618, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk855618.jpg'}, {'end': 940.121, 'src': 'embed', 'start': 917.858, 'weight': 3, 'content': [{'end': 927.062, 'text': 'nonlinearities allow you to approximate arbitrarily complex functions by kind of introducing these nonlinearities into your decision boundary.', 'start': 917.858, 'duration': 9.204}, {'end': 931.083, 'text': 'And this is what makes neural networks extremely powerful.', 'start': 928.202, 'duration': 2.881}, {'end': 934.698, 'text': "Let's understand this with a simple example.", 'start': 933.237, 'duration': 1.461}, {'end': 936.419, 'text': "And let's go back to this picture that we had before.", 'start': 934.718, 'duration': 1.701}, {'end': 940.121, 'text': 'Imagine I give you a trained network with weights w on the top right.', 'start': 937.119, 'duration': 3.002}], 'summary': 'Nonlinearities enable approximating complex functions, making neural networks powerful.', 'duration': 22.263, 'max_score': 917.858, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk917858.jpg'}, {'end': 954.07, 'src': 'heatmap', 'start': 917.858, 'weight': 0.72, 'content': [{'end': 927.062, 'text': 'nonlinearities allow you to approximate arbitrarily complex functions by kind of introducing these nonlinearities into your decision boundary.', 'start': 917.858, 'duration': 9.204}, {'end': 931.083, 'text': 'And this is what makes neural networks extremely powerful.', 'start': 928.202, 'duration': 2.881}, {'end': 934.698, 'text': "Let's understand this with a simple example.", 'start': 933.237, 'duration': 1.461}, {'end': 936.419, 'text': "And let's go back to this picture that we had before.", 'start': 934.718, 'duration': 1.701}, {'end': 940.121, 'text': 'Imagine I give you a trained network with weights w on the top right.', 'start': 937.119, 'duration': 3.002}, {'end': 943.763, 'text': 'So w here is 3 and minus 2.', 'start': 940.301, 'duration': 3.462}, {'end': 946.425, 'text': 'And the network only has two inputs, x1 and x2.', 'start': 943.763, 'duration': 2.662}, {'end': 951.508, 'text': "If we want to get the output, it's simply the same story as we had before.", 'start': 947.806, 'duration': 3.702}, {'end': 954.07, 'text': 'We multiply our inputs by those weights.', 'start': 951.948, 'duration': 2.122}], 'summary': 'Neural networks use nonlinearities to approximate complex functions, making them powerful. example: network with weights 3 and -2.', 'duration': 36.212, 'max_score': 917.858, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk917858.jpg'}, {'end': 1053.528, 'src': 'heatmap', 'start': 1011.461, 'weight': 0.723, 'content': [{'end': 1015.956, 'text': 'So it has a value of x1 of minus 1, value of x2 of 2.', 'start': 1011.461, 'duration': 4.495}, {'end': 1019.197, 'text': 'I can see visually where this lies with respect to that line.', 'start': 1015.956, 'duration': 3.241}, {'end': 1023.059, 'text': 'And in fact, this idea can be generalized a little bit more.', 'start': 1019.918, 'duration': 3.141}, {'end': 1027.521, 'text': 'If we compute that line, we get minus 6.', 'start': 1023.219, 'duration': 4.302}, {'end': 1031.363, 'text': 'So inside that, before we apply the nonlinearity, we get minus 6.', 'start': 1027.521, 'duration': 3.842}, {'end': 1039.627, 'text': 'When we apply a sigmoid nonlinearity, because sigmoid collapses everything between 0 and 1, anything greater than 0 is going to be above 0.5.', 'start': 1031.363, 'duration': 8.264}, {'end': 1044.045, 'text': 'Anything below 0 is going to be less than 0.5.', 'start': 1039.627, 'duration': 4.418}, {'end': 1051.667, 'text': "So because minus 6 is less than 0, we're going to have a very low output, this 0.002.", 'start': 1044.045, 'duration': 7.622}, {'end': 1053.528, 'text': 'And we can actually generalize this idea.', 'start': 1051.667, 'duration': 1.861}], 'summary': 'Analyzing the impact of sigmoid nonlinearity on input data, resulting in a very low output of 0.002.', 'duration': 42.067, 'max_score': 1011.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1011461.jpg'}, {'end': 1119.829, 'src': 'embed', 'start': 1095.268, 'weight': 6, 'content': [{'end': 1104.098, 'text': "The reason why it's hard to do this with deep neural networks is because you usually don't have only two inputs and you usually don't have only two weights as well.", 'start': 1095.268, 'duration': 8.83}, {'end': 1109.223, 'text': 'So as you scale up your problem, this is a simple two-dimensional problem.', 'start': 1104.738, 'duration': 4.485}, {'end': 1111.565, 'text': 'But as you scale up the size of your network,', 'start': 1109.864, 'duration': 1.701}, {'end': 1116.287, 'text': 'you could be dealing with hundreds or thousands or millions of parameters and million dimensional spaces.', 'start': 1111.565, 'duration': 4.722}, {'end': 1119.829, 'text': 'And then visualizing these type of plots becomes extremely difficult.', 'start': 1116.728, 'duration': 3.101}], 'summary': 'Visualizing deep neural networks with many parameters becomes extremely difficult.', 'duration': 24.561, 'max_score': 1095.268, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1095268.jpg'}, {'end': 1163.488, 'src': 'embed', 'start': 1135.498, 'weight': 7, 'content': [{'end': 1138.939, 'text': 'OK. so now that we have that idea of a perceptron, a single neuron,', 'start': 1135.498, 'duration': 3.441}, {'end': 1145.081, 'text': "let's start by building up neural networks now how we can use that perceptron to create full neural networks,", 'start': 1138.939, 'duration': 6.142}, {'end': 1146.962, 'text': 'and seeing how all of this story comes together.', 'start': 1145.081, 'duration': 1.881}, {'end': 1151.164, 'text': "Let's revisit this previous diagram of the perceptron.", 'start': 1148.743, 'duration': 2.421}, {'end': 1155.945, 'text': 'If there are only a few things you remember from this class, try to take away this.', 'start': 1151.564, 'duration': 4.381}, {'end': 1159.326, 'text': 'So how a perceptron works, just keep remembering this.', 'start': 1156.806, 'duration': 2.52}, {'end': 1160.347, 'text': "I'm going to keep drilling it in.", 'start': 1159.346, 'duration': 1.001}, {'end': 1161.467, 'text': 'You take your inputs.', 'start': 1160.627, 'duration': 0.84}, {'end': 1163.488, 'text': 'You apply a dot product with your weights.', 'start': 1161.967, 'duration': 1.521}], 'summary': 'Introduction to building neural networks using perceptrons and weight application.', 'duration': 27.99, 'max_score': 1135.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1135498.jpg'}, {'end': 1271.836, 'src': 'heatmap', 'start': 1201.219, 'weight': 0.7, 'content': [{'end': 1207.883, 'text': 'So our final output y is just our activation function applied on z.', 'start': 1201.219, 'duration': 6.664}, {'end': 1214.027, 'text': 'If we want to define a multi-output neural network, we simply can just add another one of these perceptrons to this picture.', 'start': 1207.883, 'duration': 6.144}, {'end': 1215.704, 'text': 'Now we have two outputs.', 'start': 1214.844, 'duration': 0.86}, {'end': 1219.506, 'text': 'One is a normal perceptron, which is y1.', 'start': 1215.945, 'duration': 3.561}, {'end': 1223.268, 'text': 'And y2 is just another normal perceptron, the same idea as before.', 'start': 1219.946, 'duration': 3.322}, {'end': 1226.169, 'text': 'They all connect to the previous layer with a different set of weights.', 'start': 1223.308, 'duration': 2.861}, {'end': 1235.454, 'text': 'And because all inputs are densely connected to all of the outputs, these type of layers are often called dense layers.', 'start': 1228.431, 'duration': 7.023}, {'end': 1245.631, 'text': "And let's take an example of how one might actually go from this nice illustration, which is very conceptual and nice and simple,", 'start': 1237.088, 'duration': 8.543}, {'end': 1251.292, 'text': 'to how you could actually implement one of these dense layers from scratch by yourselves using TensorFlow.', 'start': 1245.631, 'duration': 5.661}, {'end': 1258.754, 'text': 'So what we can do is start off by first defining our two weights.', 'start': 1253.113, 'duration': 5.641}, {'end': 1262.276, 'text': 'So we have our actual weight vector, which is w.', 'start': 1258.935, 'duration': 3.341}, {'end': 1271.836, 'text': 'And we also have our bias vector, Right? Both of these parameters are governed by the output space.', 'start': 1262.276, 'duration': 9.56}], 'summary': 'Explaining multi-output neural network with two outputs and implementing dense layers using tensorflow.', 'duration': 70.617, 'max_score': 1201.219, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1201219.jpg'}, {'end': 1352.62, 'src': 'embed', 'start': 1329.276, 'weight': 8, 'content': [{'end': 1338.699, 'text': 'And, in fact, to implement a layer like this with two outputs or a multi-output perceptron layer with two outputs,', 'start': 1329.276, 'duration': 9.423}, {'end': 1346.222, 'text': 'we can simply call this tf.keras layers dense with units equal to 2, to indicate that we have two outputs on this layer.', 'start': 1338.699, 'duration': 7.523}, {'end': 1352.62, 'text': 'And there was a whole bunch of other parameters that you could input here, such as the activation function, as well as many other things,', 'start': 1347.537, 'duration': 5.083}], 'summary': 'Implement a multi-output perceptron layer with two outputs using tf.keras layers dense with units equal to 2.', 'duration': 23.344, 'max_score': 1329.276, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1329276.jpg'}, {'end': 1427.317, 'src': 'embed', 'start': 1397.719, 'weight': 9, 'content': [{'end': 1404.385, 'text': 'each of those two transformations will have their own weight matrices, which here I call w1 and w2..', 'start': 1397.719, 'duration': 6.666}, {'end': 1407.388, 'text': 'So this corresponds to the first layer and the second layer.', 'start': 1404.425, 'duration': 2.963}, {'end': 1416.484, 'text': "If we look at a single unit inside of that hidden layer, take, for example, Z2 I'm showing here.", 'start': 1410.276, 'duration': 6.208}, {'end': 1419.788, 'text': "That's just a single perceptron, like we talked about before.", 'start': 1417.225, 'duration': 2.563}, {'end': 1427.317, 'text': "It's taking a weighted sum of all of those inputs that feed into it, and it applies a nonlinearity and feeds it onto the next layer.", 'start': 1419.808, 'duration': 7.509}], 'summary': 'Explanation of neural network layers and weighted summation process.', 'duration': 29.598, 'max_score': 1397.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1397719.jpg'}, {'end': 1513.975, 'src': 'embed', 'start': 1485.343, 'weight': 10, 'content': [{'end': 1488.364, 'text': 'you can use sequential models and just define your layers as a sequence.', 'start': 1485.343, 'duration': 3.021}, {'end': 1491.704, 'text': "And it's very nice to allow information to propagate through that model.", 'start': 1488.724, 'duration': 2.98}, {'end': 1500.607, 'text': 'Now, if we want to create a deep neural network, the idea is basically the same thing, except you just keep stacking on more of these layers.', 'start': 1494.245, 'duration': 6.362}, {'end': 1510.031, 'text': 'and to create more of a hierarchical model, ones where the final output is computed by going deeper and deeper into this representation.', 'start': 1501.983, 'duration': 8.048}, {'end': 1513.975, 'text': 'And the code looks pretty similar again.', 'start': 1511.953, 'duration': 2.022}], 'summary': 'Sequential models allow information to propagate through layers and can be stacked to create a deep neural network for hierarchical representation.', 'duration': 28.632, 'max_score': 1485.343, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1485343.jpg'}], 'start': 490.865, 'title': 'Deep learning fundamentals', 'summary': 'Discusses the significance of deep learning, contrasting it with traditional machine learning, and illustrates the concept using the example of facial detection and the evolution of deep learning algorithms. it also explores the fundamental building blocks of a neural network, including the concept of a perceptron and the challenges in training with neural networks. the necessity of studying deep learning now due to the expansion of big data and modern gpu architectures is emphasized.', 'chapters': [{'end': 646.7, 'start': 490.865, 'title': 'Why deep learning matters', 'summary': 'Discusses the significance of deep learning, contrasting it with traditional machine learning, and illustrates the concept using the example of facial detection and the evolution of deep learning algorithms, emphasizing the necessity of studying it now due to the expansion of big data and modern gpu architectures.', 'duration': 155.835, 'highlights': ['The significance of deep learning compared to traditional machine learning and the necessity of studying it now due to the expansion of big data and modern GPU architectures.', 'The concept of deep learning illustrated through the example of facial detection, emphasizing the hierarchical representation of features developed by deep learning algorithms.', 'The historical existence of fundamental building blocks and algorithms for training deep learning models.']}, {'end': 1094.827, 'start': 648.345, 'title': 'Neural network fundamentals', 'summary': 'Explores the fundamental building blocks of a neural network, including the concept of a perceptron, the role of activation functions in introducing nonlinearities into the data, and the importance of using nonlinear activation functions in neural networks to approximate complex functions.', 'duration': 446.482, 'highlights': ['Neural network fundamentals including the concept of a perceptron and activation functions are explored.', 'The importance of using nonlinear activation functions in neural networks to approximate complex functions is emphasized.', 'The streamlined process of building and deploying models using open source toolboxes like TensorFlow is mentioned.']}, {'end': 1526.617, 'start': 1095.268, 'title': 'Challenges in training with neural networks', 'summary': 'Discusses the challenges in visualizing and scaling up deep neural networks, as well as the process of building neural networks and implementing dense layers using tensorflow.', 'duration': 431.349, 'highlights': ['Scaling up neural networks can involve dealing with hundreds or thousands of parameters and million dimensional spaces, making visualization extremely difficult.', 'The process of building neural networks involves creating full neural networks using perceptrons and implementing dense layers, with each input densely connected to all outputs.', 'Implementing dense layers using TensorFlow involves defining weights and biases, performing forward propagation, and using the tf.keras.layers.dense function to create multi-output perceptron layers.', 'The concept of hidden layers in a single layered neural network involves transformations between inputs and hidden layers, as well as between hidden layers and output layers, each with their own weight matrices.', 'Creating deep neural networks involves stacking multiple layers to create a hierarchical model for computing the final output.']}], 'duration': 1035.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk490865.jpg', 'highlights': ['The significance of deep learning compared to traditional machine learning and the necessity of studying it now due to the expansion of big data and modern GPU architectures.', 'The concept of deep learning illustrated through the example of facial detection, emphasizing the hierarchical representation of features developed by deep learning algorithms.', 'Neural network fundamentals including the concept of a perceptron and activation functions are explored.', 'The importance of using nonlinear activation functions in neural networks to approximate complex functions is emphasized.', 'The historical existence of fundamental building blocks and algorithms for training deep learning models.', 'The streamlined process of building and deploying models using open source toolboxes like TensorFlow is mentioned.', 'Scaling up neural networks can involve dealing with hundreds or thousands of parameters and million dimensional spaces, making visualization extremely difficult.', 'The process of building neural networks involves creating full neural networks using perceptrons and implementing dense layers, with each input densely connected to all outputs.', 'Implementing dense layers using TensorFlow involves defining weights and biases, performing forward propagation, and using the tf.keras.layers.dense function to create multi-output perceptron layers.', 'The concept of hidden layers in a single layered neural network involves transformations between inputs and hidden layers, as well as between hidden layers and output layers, each with their own weight matrices.', 'Creating deep neural networks involves stacking multiple layers to create a hierarchical model for computing the final output.']}, {'end': 2344.601, 'segs': [{'end': 1840.504, 'src': 'heatmap', 'start': 1672.247, 'weight': 0, 'content': [{'end': 1674.068, 'text': "It's just seeing some random numbers.", 'start': 1672.247, 'duration': 1.821}, {'end': 1678.031, 'text': 'It has no concept of how other people in the class have done so far.', 'start': 1674.289, 'duration': 3.742}, {'end': 1681.414, 'text': 'So what we have to do to this network first is train it.', 'start': 1678.612, 'duration': 2.802}, {'end': 1684.557, 'text': 'And we have to teach it how to perform this task.', 'start': 1681.875, 'duration': 2.682}, {'end': 1688.649, 'text': "Until we teach it, it's just like a baby that doesn't know anything.", 'start': 1685.748, 'duration': 2.901}, {'end': 1690.03, 'text': "So it's just entered the world.", 'start': 1688.73, 'duration': 1.3}, {'end': 1693.612, 'text': 'It has no concepts or no idea of how to solve this task.', 'start': 1690.57, 'duration': 3.042}, {'end': 1694.833, 'text': 'And we have to teach it that.', 'start': 1693.992, 'duration': 0.841}, {'end': 1701.636, 'text': "Now, how do we do that? The idea here is that first, we have to tell the network when it's wrong.", 'start': 1696.493, 'duration': 5.143}, {'end': 1705.378, 'text': "So we have to quantify what's called its loss or its error.", 'start': 1702.136, 'duration': 3.242}, {'end': 1714.466, 'text': 'And to do that, we actually just take our prediction, or what the network predicts, and we compare it to what the true answer was.', 'start': 1707.423, 'duration': 7.043}, {'end': 1722.309, 'text': "If there's a big discrepancy between the prediction and the true answer, we can tell the network, hey, you made a big mistake.", 'start': 1715.326, 'duration': 6.983}, {'end': 1723.79, 'text': "So there's a big error.", 'start': 1722.909, 'duration': 0.881}, {'end': 1724.87, 'text': "It's a big loss.", 'start': 1724.15, 'duration': 0.72}, {'end': 1731.053, 'text': 'And you should try and fix your answer to move closer towards the true answer, which it should be.', 'start': 1725.77, 'duration': 5.283}, {'end': 1741.863, 'text': "Now you can imagine, if you don't have just one student, but now you have many students, the total loss.", 'start': 1734.318, 'duration': 7.545}, {'end': 1744.785, 'text': "let's call it here the empirical risk or the objective function.", 'start': 1741.863, 'duration': 2.922}, {'end': 1746.186, 'text': 'It has many different names.', 'start': 1745.166, 'duration': 1.02}, {'end': 1750.589, 'text': "It's just the average of all of those individual losses.", 'start': 1746.587, 'duration': 4.002}, {'end': 1755.833, 'text': 'So the individual loss is loss that takes as input your prediction and your actual.', 'start': 1750.629, 'duration': 5.204}, {'end': 1759.656, 'text': "That's telling you how wrong that single example is.", 'start': 1757.234, 'duration': 2.422}, {'end': 1766.484, 'text': 'And then the final, the total loss, is just the average of all of those individual student losses.', 'start': 1760.68, 'duration': 5.804}, {'end': 1775.429, 'text': "So if we look at the problem of binary classification, which is the case that we're actually caring about in this example.", 'start': 1770.046, 'duration': 5.383}, {'end': 1784.136, 'text': "So we're asking a question, will I pass the class, yes or no, binary classification? we can use what is called as the softmax cross entropy loss.", 'start': 1775.469, 'duration': 8.667}, {'end': 1787.859, 'text': "And for those of you who aren't familiar with cross entropy,", 'start': 1784.457, 'duration': 3.402}, {'end': 1797.285, 'text': "this was actually a formulation introduced by Claude Shannon here at MIT during his master's thesis as well.", 'start': 1787.859, 'duration': 9.426}, {'end': 1799.567, 'text': 'And this was about 50 years ago.', 'start': 1798.106, 'duration': 1.461}, {'end': 1801.689, 'text': "It's still being used very prevalently today.", 'start': 1799.627, 'duration': 2.062}, {'end': 1807.806, 'text': 'And the idea is it just, again, compares how different these two distributions are.', 'start': 1803.104, 'duration': 4.702}, {'end': 1815.188, 'text': 'So you have a distribution of how likely you think the student is going to pass, and you have the true distribution of if the student passed or not.', 'start': 1807.826, 'duration': 7.362}, {'end': 1822.291, 'text': 'You can compare the difference between those two distributions, and that tells you the loss that the network incurs on that example.', 'start': 1815.689, 'duration': 6.602}, {'end': 1829.838, 'text': "Now, let's assume that, instead of a classification problem, we have a regression problem where,", 'start': 1823.834, 'duration': 6.004}, {'end': 1835.281, 'text': "instead of predicting if you're going to pass or fail the class, you want to predict the final grade that you're going to get.", 'start': 1829.838, 'duration': 5.443}, {'end': 1840.504, 'text': "So now it's not a yes-no answer problem anymore, but instead it's a.", 'start': 1835.761, 'duration': 4.743}], 'summary': 'Training a network involves quantifying and minimizing its error to guide it in making better predictions.', 'duration': 42.219, 'max_score': 1672.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1672247.jpg'}, {'end': 1797.285, 'src': 'embed', 'start': 1770.046, 'weight': 2, 'content': [{'end': 1775.429, 'text': "So if we look at the problem of binary classification, which is the case that we're actually caring about in this example.", 'start': 1770.046, 'duration': 5.383}, {'end': 1784.136, 'text': "So we're asking a question, will I pass the class, yes or no, binary classification? we can use what is called as the softmax cross entropy loss.", 'start': 1775.469, 'duration': 8.667}, {'end': 1787.859, 'text': "And for those of you who aren't familiar with cross entropy,", 'start': 1784.457, 'duration': 3.402}, {'end': 1797.285, 'text': "this was actually a formulation introduced by Claude Shannon here at MIT during his master's thesis as well.", 'start': 1787.859, 'duration': 9.426}], 'summary': 'Binary classification uses softmax cross entropy loss for decision-making.', 'duration': 27.239, 'max_score': 1770.046, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1770046.jpg'}, {'end': 1903.833, 'src': 'heatmap', 'start': 1869.445, 'weight': 0.718, 'content': [{'end': 1873.528, 'text': "That's the loss that the network should try to optimize and try to minimize.", 'start': 1869.445, 'duration': 4.083}, {'end': 1875.441, 'text': 'So OK.', 'start': 1874.881, 'duration': 0.56}, {'end': 1881.684, 'text': 'so now that we have all this information with the loss function and how to actually quantify the error of the neural network,', 'start': 1875.441, 'duration': 6.243}, {'end': 1890.869, 'text': "let's take this and understand how to train our model to actually find those weights that it needs to use for its prediction.", 'start': 1881.684, 'duration': 9.185}, {'end': 1896.167, 'text': 'So W is what we want to find out.', 'start': 1893.024, 'duration': 3.143}, {'end': 1897.528, 'text': 'W is the set of weights.', 'start': 1896.187, 'duration': 1.341}, {'end': 1903.833, 'text': 'And we want to find the optimal set of weights that tries to minimize this total loss over our entire test set.', 'start': 1897.628, 'duration': 6.205}], 'summary': 'Optimize network loss, minimize error, find optimal weights.', 'duration': 34.388, 'max_score': 1869.445, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1869445.jpg'}, {'end': 2072.989, 'src': 'embed', 'start': 2045.912, 'weight': 3, 'content': [{'end': 2053.775, 'text': "because you're taking a gradient and you're descending down that landscape by starting to initialize our weights randomly.", 'start': 2045.912, 'duration': 7.863}, {'end': 2058.858, 'text': 'We compute the gradient, dj, with respect to all of our weights.', 'start': 2055.616, 'duration': 3.242}, {'end': 2069.507, 'text': 'Then we update our weights in the opposite direction of that gradient and take a small step, which we call here eta, of that gradient.', 'start': 2060.438, 'duration': 9.069}, {'end': 2072.989, 'text': 'And this is referred to as the learning rate.', 'start': 2070.067, 'duration': 2.922}], 'summary': 'Optimize weights using gradient descent with random initialization.', 'duration': 27.077, 'max_score': 2045.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2045912.jpg'}, {'end': 2227.388, 'src': 'heatmap', 'start': 2185.176, 'weight': 0.717, 'content': [{'end': 2189.477, 'text': "So that's the gradient that we care about, the gradient of our loss with respect to W2..", 'start': 2185.176, 'duration': 4.301}, {'end': 2194.734, 'text': 'Now to evaluate this, we can just apply the chain rule in calculus.', 'start': 2190.97, 'duration': 3.764}, {'end': 2206.646, 'text': 'So we can split this up into the gradient of our loss with respect to our output y multiplied by the gradient of our output y with respect to w2.', 'start': 2195.354, 'duration': 11.292}, {'end': 2215.659, 'text': "Now if we want to repeat this process for a different way in the neural network, let's say now w1, not w2.", 'start': 2209.554, 'duration': 6.105}, {'end': 2218.561, 'text': 'Now we replace w1 on both sides.', 'start': 2216.5, 'duration': 2.061}, {'end': 2219.982, 'text': 'We also apply the chain rule.', 'start': 2218.621, 'duration': 1.361}, {'end': 2227.388, 'text': "But now you're going to notice that the gradient of y with respect to w1 is also not directly computable.", 'start': 2220.243, 'duration': 7.145}], 'summary': 'Using the chain rule to compute gradients for w1 and w2 in neural networks.', 'duration': 42.212, 'max_score': 2185.176, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2185176.jpg'}, {'end': 2333.816, 'src': 'embed', 'start': 2302.78, 'weight': 1, 'content': [{'end': 2304.922, 'text': "OK, and that's the backprop algorithm.", 'start': 2302.78, 'duration': 2.142}, {'end': 2307.655, 'text': 'In theory, very simple.', 'start': 2305.854, 'duration': 1.801}, {'end': 2309.957, 'text': "It's just an application of the chain rule in essence.", 'start': 2307.715, 'duration': 2.242}, {'end': 2317.181, 'text': "But now let's touch on some of the insights from training and how you can use the backprop algorithm to train these networks in practice.", 'start': 2310.677, 'duration': 6.504}, {'end': 2323.228, 'text': 'Optimization of neural networks is incredibly tough in practice.', 'start': 2319.665, 'duration': 3.563}, {'end': 2328.272, 'text': "So it's not as simple as the picture I showed you on the colorful one on the previous slide.", 'start': 2323.608, 'duration': 4.664}, {'end': 2333.816, 'text': "Here's an illustration from a paper that came out about two or three years ago now,", 'start': 2329.033, 'duration': 4.783}], 'summary': 'Backprop algorithm for neural network training is complex and challenging in practice.', 'duration': 31.036, 'max_score': 2302.78, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2302780.jpg'}], 'start': 1526.637, 'title': 'Neural network training', 'summary': 'Discusses the application of neural networks in solving real-world problems, such as predicting the outcome of passing a class and the process of training the network to minimize error and improve accuracy, with examples of probabilities and loss functions used in training.', 'chapters': [{'end': 1766.484, 'start': 1526.637, 'title': 'Neural networks and training', 'summary': "Discusses the application of neural networks in solving real-world problems, such as predicting the outcome of passing a class, and the process of training the network to minimize error and improve accuracy, with an example of a neural network predicting a student's chance of passing a class with 10% probability, which was inaccurate due to the network being untrained and having incorrect weights.", 'duration': 239.847, 'highlights': ['The network predicted a 10% probability of passing the class, but the actual outcome was passing, exposing the inaccuracy due to the untrained network with incorrect weights.', 'The process of training the network involves quantifying its loss or error by comparing its predictions with the true answers, aiming to minimize the discrepancy and improve accuracy.', 'The network needs to be taught and trained to understand the task, similar to a baby learning about the world, and it requires the concept of how to solve the task to be taught to it.']}, {'end': 2092.225, 'start': 1770.046, 'title': 'Neural network training and loss functions', 'summary': 'Explains the use of softmax cross entropy loss for binary classification and mean squared error loss for regression problems, and the process of gradient descent in training neural networks to minimize the loss function by iteratively updating the weights.', 'duration': 322.179, 'highlights': ['The chapter explains the use of softmax cross entropy loss for binary classification, introduced by Claude Shannon at MIT, and mean squared error loss for regression problems.', 'The process of gradient descent is described, involving iteratively updating weights to minimize the loss function by computing the gradient, updating the weights in the opposite direction of the gradient, and determining the learning rate.']}, {'end': 2344.601, 'start': 2092.565, 'title': 'Backpropagation basics', 'summary': 'Explains the backpropagation algorithm, which involves computing gradients, applying the chain rule, and propagating error signals, enabling automatic differentiation and making training neural networks easier, with insights into the challenges of optimizing neural networks in practice.', 'duration': 252.036, 'highlights': ['The backpropagation algorithm involves computing gradients using the chain rule and propagating error signals from output to input, enabling automatic differentiation in popular deep learning frameworks.', 'Optimizing neural networks is incredibly tough in practice, as illustrated by the complex landscape of a neural network with millions of parameters visualized in two-dimensional space.', 'The gradient of the loss with respect to the weights determines how much a small change in the weights will impact the loss, providing crucial information for training neural networks.']}], 'duration': 817.964, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk1526637.jpg', 'highlights': ['The process of training the network involves quantifying its loss or error by comparing its predictions with the true answers, aiming to minimize the discrepancy and improve accuracy.', 'The backpropagation algorithm involves computing gradients using the chain rule and propagating error signals from output to input, enabling automatic differentiation in popular deep learning frameworks.', 'The chapter explains the use of softmax cross entropy loss for binary classification, introduced by Claude Shannon at MIT, and mean squared error loss for regression problems.', 'The process of gradient descent involves iteratively updating weights to minimize the loss function by computing the gradient, updating the weights in the opposite direction of the gradient, and determining the learning rate.', 'The network needs to be taught and trained to understand the task, similar to a baby learning about the world, and it requires the concept of how to solve the task to be taught to it.']}, {'end': 3167.053, 'segs': [{'end': 2497.959, 'src': 'embed', 'start': 2467.878, 'weight': 0, 'content': [{'end': 2474.204, 'text': 'This means that the learning rate is no longer fixed, but it can now increase or decrease throughout training.', 'start': 2467.878, 'duration': 6.326}, {'end': 2477.367, 'text': 'So as training progresses, your learning rate may speed up.', 'start': 2474.364, 'duration': 3.003}, {'end': 2479.168, 'text': 'You may take more aggressive steps.', 'start': 2477.667, 'duration': 1.501}, {'end': 2484.753, 'text': 'You may take smaller steps as you get closer to the local minima so that you really converge on that point.', 'start': 2479.188, 'duration': 5.565}, {'end': 2489.538, 'text': 'And there are many options here of how you might want to design this adaptive algorithm.', 'start': 2485.914, 'duration': 3.624}, {'end': 2497.959, 'text': 'And this has been a huge or widely studied field in optimization theory for machine learning and deep learning.', 'start': 2490.509, 'duration': 7.45}], 'summary': 'Adaptive learning rate adjusts during training, optimizing convergence to local minima. widely studied in ml and dl.', 'duration': 30.081, 'max_score': 2467.878, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2467878.jpg'}, {'end': 2540.638, 'src': 'embed', 'start': 2508.447, 'weight': 4, 'content': [{'end': 2511.873, 'text': 'So SGD is just that vanilla gradient descent that I showed you before.', 'start': 2508.447, 'duration': 3.426}, {'end': 2512.914, 'text': "That's the first one.", 'start': 2511.893, 'duration': 1.021}, {'end': 2519.625, 'text': 'All of the others are all adaptive learning rates, which means that they change their learning rate during training itself.', 'start': 2513.415, 'duration': 6.21}, {'end': 2522.951, 'text': 'So they can increase or decrease depending on how the optimization is going.', 'start': 2519.725, 'duration': 3.226}, {'end': 2532.114, 'text': "And during your labs, we really encourage you again to try out some of these different optimization schemes, see what works, what doesn't work.", 'start': 2524.77, 'duration': 7.344}, {'end': 2534.055, 'text': 'A lot of it is problem dependent.', 'start': 2532.654, 'duration': 1.401}, {'end': 2540.638, 'text': 'There are some heuristics that you can get, but we want you to really gain those heuristics yourselves through the course of the labs.', 'start': 2534.175, 'duration': 6.463}], 'summary': 'Various optimization schemes adapt learning rates during training to improve optimization process.', 'duration': 32.191, 'max_score': 2508.447, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2508447.jpg'}, {'end': 2649.813, 'src': 'embed', 'start': 2618.563, 'weight': 2, 'content': [{'end': 2620.987, 'text': 'And hopefully, you should fit your data.', 'start': 2618.563, 'duration': 2.424}, {'end': 2635.251, 'text': 'Now I want to continue to talk about some tips for training these networks in practice and focus on a very powerful idea of batching your data into mini batches.', 'start': 2624.204, 'duration': 11.047}, {'end': 2639.594, 'text': "So to do this, let's revisit the gradient descent algorithm.", 'start': 2637.032, 'duration': 2.562}, {'end': 2645.872, 'text': 'This gradient is actually very computationally expensive to compute in practice.', 'start': 2640.971, 'duration': 4.901}, {'end': 2649.813, 'text': 'So using the backprop algorithm is a very expensive idea in practice.', 'start': 2645.912, 'duration': 3.901}], 'summary': 'Training networks with mini batches reduces computational expense.', 'duration': 31.25, 'max_score': 2618.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2618563.jpg'}, {'end': 2936.104, 'src': 'embed', 'start': 2906.192, 'weight': 3, 'content': [{'end': 2910.973, 'text': "Now, as we've seen before, this is actually crucial for our models to be able to generalize to our test data.", 'start': 2906.192, 'duration': 4.781}, {'end': 2912.914, 'text': 'So this is very important.', 'start': 2911.614, 'duration': 1.3}, {'end': 2919.336, 'text': 'The most popular regularization technique in deep learning is this very basic idea of dropout.', 'start': 2913.354, 'duration': 5.982}, {'end': 2928.199, 'text': "Now, the idea of dropout is actually let's start with by revisiting this picture of a neural network that we had introduced previously.", 'start': 2919.596, 'duration': 8.603}, {'end': 2936.104, 'text': 'In dropout, during training, we randomly set some of these activations of the hidden neurons to 0 with some probability.', 'start': 2929.039, 'duration': 7.065}], 'summary': 'Regularization technique dropout crucial for generalization in deep learning.', 'duration': 29.912, 'max_score': 2906.192, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2906192.jpg'}], 'start': 2345.341, 'title': 'Neural network training fundamentals', 'summary': 'Discusses the fundamentals of neural networks, emphasizing the importance of mini-batching, regularization, and adaptive learning rates in practical model training. it focuses on using mini-batches to improve computational efficiency and accuracy, and techniques like dropout and early stopping for regularization.', 'chapters': [{'end': 2548.782, 'start': 2345.341, 'title': 'Challenges of gradient descent', 'summary': 'Discusses the challenges of applying gradient descent in neural networks, particularly in setting the learning rate, which can lead to getting stuck in local minima or overshooting, and explores the concept of adaptive learning rates. it also emphasizes the importance of experimenting with different optimization schemes in practical applications.', 'duration': 203.441, 'highlights': ['The learning rate in gradient descent determines the step taken in the direction of the gradient, and setting it is crucial for performance, with a small learning rate leading to getting stuck in local minima and a large one causing divergence.', 'Adaptive learning rates can change throughout training, allowing for more aggressive or smaller steps based on the optimization progress or landscape, and this concept has been widely studied in machine learning and deep learning.', 'Experimenting with different optimization schemes in practical applications is important, as it is problem-dependent and requires gaining heuristics through hands-on experience.', 'The landscape of neural networks is complex, with many local minima where the gradient descent algorithm could get stuck, making it a huge challenge to apply in practice.', 'Vanilla gradient descent (SGD) is just one of the optimization schemes, while the others are all adaptive learning rates that change during training based on the optimization progress.']}, {'end': 3167.053, 'start': 2550.731, 'title': 'Neural network training fundamentals', 'summary': 'Discusses the fundamentals of neural networks, emphasizing the importance of mini-batching, regularization, and adaptive learning rates in practical model training, with a focus on using mini-batches to improve computational efficiency and accuracy, and techniques like dropout and early stopping for regularization.', 'duration': 616.322, 'highlights': ['The importance of mini-batching in neural network training', 'Regularization techniques in neural networks: Dropout and early stopping', 'Fundamentals of neural networks and practical training aspects']}], 'duration': 821.712, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/njKP3FqW3Sk/pics/njKP3FqW3Sk2345341.jpg', 'highlights': ['Adaptive learning rates can change throughout training, allowing for more aggressive or smaller steps based on the optimization progress or landscape.', 'The learning rate in gradient descent determines the step taken in the direction of the gradient, and setting it is crucial for performance.', 'The importance of mini-batching in neural network training', 'Regularization techniques in neural networks: Dropout and early stopping', 'Vanilla gradient descent (SGD) is just one of the optimization schemes, while the others are all adaptive learning rates that change during training based on the optimization progress.']}], 'highlights': ['The MIT 6S191 boot camp on deep learning has been taught for the fourth consecutive year, with increasing participation each year.', 'Deep learning is revolutionizing various fields, including robotics and medicine, and the course aims to provide fundamentals and practical knowledge for implementing deep learning algorithms.', 'The class focuses on teaching algorithms how to learn a task directly from raw data, with a practical emphasis on implementing state-of-the-art deep learning algorithms in TensorFlow.', 'The speech and video demonstration were created using deep learning and artificial intelligence, showcasing the power of deep learning in voice synthesis and video dialogue replacement.', 'Participants have options to propose a project in groups or write a one-page paper review, with prizes including NVIDIA GPUs, Google Home, and SSD cards.', 'The course offers software labs after each day of lectures to build upon the knowledge gained during the lectures.', 'The significance of deep learning compared to traditional machine learning and the necessity of studying it now due to the expansion of big data and modern GPU architectures.', 'The concept of deep learning illustrated through the example of facial detection, emphasizing the hierarchical representation of features developed by deep learning algorithms.', 'Neural network fundamentals including the concept of a perceptron and activation functions are explored.', 'The importance of using nonlinear activation functions in neural networks to approximate complex functions is emphasized.', 'The historical existence of fundamental building blocks and algorithms for training deep learning models.', 'The streamlined process of building and deploying models using open source toolboxes like TensorFlow is mentioned.', 'The process of training the network involves quantifying its loss or error by comparing its predictions with the true answers, aiming to minimize the discrepancy and improve accuracy.', 'The backpropagation algorithm involves computing gradients using the chain rule and propagating error signals from output to input, enabling automatic differentiation in popular deep learning frameworks.', 'The chapter explains the use of softmax cross entropy loss for binary classification, introduced by Claude Shannon at MIT, and mean squared error loss for regression problems.', 'The process of gradient descent involves iteratively updating weights to minimize the loss function by computing the gradient, updating the weights in the opposite direction of the gradient, and determining the learning rate.', 'The network needs to be taught and trained to understand the task, similar to a baby learning about the world, and it requires the concept of how to solve the task to be taught to it.', 'Adaptive learning rates can change throughout training, allowing for more aggressive or smaller steps based on the optimization progress or landscape.', 'The learning rate in gradient descent determines the step taken in the direction of the gradient, and setting it is crucial for performance.', 'The importance of mini-batching in neural network training', 'Regularization techniques in neural networks: Dropout and early stopping', 'Vanilla gradient descent (SGD) is just one of the optimization schemes, while the others are all adaptive learning rates that change during training based on the optimization progress.']}