title

How Deep Neural Networks Work - Full Course for Beginners

description

Even if you are completely new to neural networks, this course will get you comfortable with the concepts and math behind them.
Neural networks are at the core of what we are calling Artificial Intelligence today. They can seem impenetrable, even mystical, if you are trying to understand them for the first time, but they don't have to.
⭐️ Contents ⭐️
⌨️ (0:00:00) How neural networks work
⌨️ (0:24:13) What neural networks can learn and how they learn it
⌨️ (0:51:37) How convolutional neural networks (CNNs) work
⌨️ (1:16:55) How recurrent neural networks (RNNs) and long-short-term memory (LSTM) work
⌨️ (1:42:49) Deep learning demystified
⌨️ (2:03:33) Getting closer to human intelligence through robotics
⌨️ (2:49:18) How CNNs work, in depth
🎥 Lectures by Brandon Rohrer. Check out his YouTube channel: https://www.youtube.com/user/BrandonRohrer
🔗 Find more courses from Brandon at https://end-to-end-machine-learning.teachable.com/
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detail

{'title': 'How Deep Neural Networks Work - Full Course for Beginners', 'heatmap': [{'end': 843.112, 'start': 689.67, 'weight': 0.849}, {'end': 13844.937, 'start': 13713.856, 'weight': 1}], 'summary': 'Course on deep neural networks covers topics including neural network fundamentals, convolutional neural networks, lstm, deep learning basics, ai applications, and optimization in machine learning, providing comprehensive insights into pattern recognition and the capabilities and limitations of ai in various fields.', 'chapters': [{'end': 50.42, 'segs': [{'end': 50.42, 'src': 'embed', 'start': 2.332, 'weight': 0, 'content': [{'end': 5.215, 'text': 'Neural networks are good for learning lots of different types of patterns.', 'start': 2.332, 'duration': 2.883}, {'end': 11.2, 'text': 'To give an example of how this would work, imagine you had a four pixel camera.', 'start': 7.316, 'duration': 3.884}, {'end': 14.383, 'text': 'So not four megapixels, but just four pixels.', 'start': 11.96, 'duration': 2.423}, {'end': 16.324, 'text': 'And it was only black and white.', 'start': 15.043, 'duration': 1.281}, {'end': 26.693, 'text': 'And you wanted to go around and take pictures of things and determine automatically then whether these pictures were of solid,', 'start': 17.585, 'duration': 9.108}, {'end': 28.255, 'text': 'all white or all dark image.', 'start': 26.693, 'duration': 1.562}, {'end': 33.83, 'text': 'vertical line or a diagonal line or a horizontal line.', 'start': 29.448, 'duration': 4.382}, {'end': 40.874, 'text': "This is tricky because you can't do this with simple rules about the brightness of the pixels.", 'start': 35.571, 'duration': 5.303}, {'end': 50.42, 'text': "Both of these are horizontal lines, but if you tried to make a rule about which pixel was bright and which was dark, you wouldn't be able to do it.", 'start': 41.995, 'duration': 8.425}], 'summary': 'Neural networks can learn diverse patterns, such as identifying images with specific characteristics like lines, using a minimal four-pixel camera setup.', 'duration': 48.088, 'max_score': 2.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM2332.jpg'}], 'start': 2.332, 'title': 'Neural network for pattern recognition', 'summary': 'Delves into the application of neural networks in learning different patterns, demonstrated through a four-pixel camera example, and emphasizes the challenges in identifying specific patterns using simple rules.', 'chapters': [{'end': 50.42, 'start': 2.332, 'title': 'Neural network for pattern recognition', 'summary': 'Discusses how neural networks can learn different patterns, illustrated with a four-pixel camera example, highlighting the challenges of identifying specific patterns using simple rules.', 'duration': 48.088, 'highlights': ['Neural networks are effective for learning various patterns, as demonstrated with a four-pixel camera example.', 'Challenges arise when trying to identify specific patterns using simple rules due to limitations in pixel brightness-based rules.']}], 'duration': 48.088, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM2332.jpg', 'highlights': ['Neural networks are effective for learning various patterns, as demonstrated with a four-pixel camera example.', 'Challenges arise when trying to identify specific patterns using simple rules due to limitations in pixel brightness-based rules.']}, {'end': 1458.571, 'segs': [{'end': 107.648, 'src': 'embed', 'start': 53.945, 'weight': 12, 'content': [{'end': 59.187, 'text': 'So to do this with the neural network, you start by taking all of your inputs, in this case our four pixels,', 'start': 53.945, 'duration': 5.242}, {'end': 61.168, 'text': 'and you break them out into input neurons.', 'start': 59.187, 'duration': 1.981}, {'end': 67.77, 'text': 'And you assign a number to each of these, depending on the brightness or darkness of the pixel.', 'start': 62.388, 'duration': 5.382}, {'end': 74.933, 'text': 'Plus one is all the way white, minus one is all the way black, and then gray is zero, right in the middle.', 'start': 68.731, 'duration': 6.202}, {'end': 85.377, 'text': "So these values, once you have them broken out and listed like this on the input neurons, it's also called the input vector or array.", 'start': 77.813, 'duration': 7.564}, {'end': 90.96, 'text': "It's just a list of numbers that represents your inputs right now.", 'start': 85.797, 'duration': 5.163}, {'end': 99.225, 'text': "It's a useful notion to think about the receptive field of a neuron.", 'start': 93.722, 'duration': 5.503}, {'end': 107.648, 'text': 'All this means is what set of inputs makes the value of this neuron as high as it can possibly be.', 'start': 100.404, 'duration': 7.244}], 'summary': 'Using a neural network with input neurons, assigning values based on pixel brightness, to represent inputs and receptive field of a neuron.', 'duration': 53.703, 'max_score': 53.945, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM53945.jpg'}, {'end': 229.504, 'src': 'embed', 'start': 170.3, 'weight': 11, 'content': [{'end': 177.286, 'text': "So for instance, if something has a weight of minus 1, it's multiplied and you get the negative of it, and that's added in.", 'start': 170.3, 'duration': 6.986}, {'end': 180.71, 'text': "If something has a weight of 0, then it's effectively ignored.", 'start': 177.547, 'duration': 3.163}, {'end': 184.791, 'text': "So here's what those weighted connections might look like.", 'start': 182.349, 'duration': 2.442}, {'end': 194.197, 'text': "You'll notice that after the values of the input neurons are weighted and added, the final value is completely different.", 'start': 185.611, 'duration': 8.586}, {'end': 206.746, 'text': "Graphically, it's convenient to represent these weights as white links being positive weights, black links being negative weights,", 'start': 198.38, 'duration': 8.366}, {'end': 212.237, 'text': 'And the thickness of the line is roughly proportional to the magnitude of the weight.', 'start': 207.475, 'duration': 4.762}, {'end': 221.701, 'text': 'Then after you add the weighted input neurons, they get squashed.', 'start': 216.439, 'duration': 5.262}, {'end': 224.102, 'text': "And I'll show you what that means.", 'start': 222.941, 'duration': 1.161}, {'end': 227.523, 'text': 'You have a sigmoid squashing function.', 'start': 225.022, 'duration': 2.501}, {'end': 229.504, 'text': 'Sigmoid just means S-shaped.', 'start': 227.803, 'duration': 1.701}], 'summary': 'Neural network weights can modify input values, producing different final values; represented graphically with varying thickness and color in links.', 'duration': 59.204, 'max_score': 170.3, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM170300.jpg'}, {'end': 398.455, 'src': 'embed', 'start': 363.361, 'weight': 9, 'content': [{'end': 376.228, 'text': 'the receptive field, the pixel values that make that first layer neuron as large as it can possibly be, look now like pairs of pixels,', 'start': 363.361, 'duration': 12.867}, {'end': 382.552, 'text': 'either all white or a mixture of white and black, depending on the weights.', 'start': 376.228, 'duration': 6.324}, {'end': 396.535, 'text': 'So, for instance, this neuron here is attached to this input pixel, which is upper left, and this input pixel, which is lower left,', 'start': 385.113, 'duration': 11.422}, {'end': 398.455, 'text': 'and both of those weights are positive.', 'start': 396.535, 'duration': 1.92}], 'summary': 'Neuron receptive field consists of pairs of pixels, with positive weights.', 'duration': 35.094, 'max_score': 363.361, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM363361.jpg'}, {'end': 464.545, 'src': 'embed', 'start': 432.438, 'weight': 7, 'content': [{'end': 441.603, 'text': 'Now, because we were careful of how we created that first layer, its values look a lot like input values.', 'start': 432.438, 'duration': 9.165}, {'end': 451.968, 'text': 'And we can turn right around and create another layer on top of it the exact same way with the output of one layer being the input to the next layer.', 'start': 442.383, 'duration': 9.585}, {'end': 458.972, 'text': 'And we can repeat this three times or seven times or 700 times for additional layers.', 'start': 452.949, 'duration': 6.023}, {'end': 464.545, 'text': 'Each time the receptive fields get even more complex.', 'start': 461.403, 'duration': 3.142}], 'summary': 'Layer repetition creates complex receptive fields for input values.', 'duration': 32.107, 'max_score': 432.438, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM432438.jpg'}, {'end': 525.38, 'src': 'embed', 'start': 496.994, 'weight': 8, 'content': [{'end': 502.415, 'text': "So just to mix things up, we'll create a new layer, but if you notice, our squashing function isn't there anymore.", 'start': 496.994, 'duration': 5.421}, {'end': 506.016, 'text': 'We have something new called a rectified linear unit.', 'start': 502.815, 'duration': 3.201}, {'end': 510.297, 'text': 'This is another popular neuron type.', 'start': 506.896, 'duration': 3.401}, {'end': 519.934, 'text': 'So you do your weighted sum of all your inputs, and instead of squashing, you do rectified linear units.', 'start': 511.348, 'duration': 8.586}, {'end': 521.256, 'text': 'You rectify it.', 'start': 520.535, 'duration': 0.721}, {'end': 525.38, 'text': 'So if it is negative, you make the value zero.', 'start': 521.756, 'duration': 3.624}], 'summary': 'Introducing rectified linear units for neuron activation in a new layer.', 'duration': 28.386, 'max_score': 496.994, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM496994.jpg'}, {'end': 618.034, 'src': 'embed', 'start': 587.054, 'weight': 0, 'content': [{'end': 592.477, 'text': 'As we propagate that to our input layer, this is what those values would look like.', 'start': 587.054, 'duration': 5.423}, {'end': 595.238, 'text': 'The top pixels, the bottom pixels.', 'start': 593.617, 'duration': 1.621}, {'end': 609.372, 'text': 'As we move that to our first layer, we can see the combination of a dark pixel and a light pixel sum together get us 0, gray.', 'start': 596.909, 'duration': 12.463}, {'end': 618.034, 'text': 'Whereas down here, we have the combination of a dark pixel plus a light pixel with a negative weight.', 'start': 611.633, 'duration': 6.401}], 'summary': 'Input layer processes pixels to create combinations, including negative weights.', 'duration': 30.98, 'max_score': 587.054, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM587054.jpg'}, {'end': 843.112, 'src': 'heatmap', 'start': 689.67, 'weight': 0.849, 'content': [{'end': 694.074, 'text': 'Because we have a rectified linear unit, negative values become zero.', 'start': 689.67, 'duration': 4.404}, {'end': 696.396, 'text': 'So now it is zero again, two.', 'start': 694.994, 'duration': 1.402}, {'end': 699.318, 'text': 'But this one gets rectified and becomes positive.', 'start': 696.996, 'duration': 2.322}, {'end': 701.1, 'text': 'Negative times a negative is positive.', 'start': 699.578, 'duration': 1.522}, {'end': 709.94, 'text': "And so when we finally get to the output, We can see they're all zero except for this horizontal, which is positive, and that's the answer.", 'start': 702.301, 'duration': 7.639}, {'end': 715.002, 'text': 'Our neural network said this is an image of a horizontal line.', 'start': 710.02, 'duration': 4.982}, {'end': 721.345, 'text': "Now, neural networks usually aren't that good, not that clean.", 'start': 717.884, 'duration': 3.461}, {'end': 733.564, 'text': "So there's a notion of with an input, what is truth? In this case, the truth is this has a zero for all of these values, but a one for horizontal.", 'start': 722.326, 'duration': 11.238}, {'end': 736.105, 'text': "It's not solid, it's not vertical, it's not diagonal.", 'start': 733.764, 'duration': 2.341}, {'end': 737.866, 'text': 'Yes, it is horizontal.', 'start': 736.706, 'duration': 1.16}, {'end': 744.329, 'text': 'An arbitrary neural network will give answers that are not exactly truth.', 'start': 739.707, 'duration': 4.622}, {'end': 746.47, 'text': 'It might be off by a little or a lot.', 'start': 744.649, 'duration': 1.821}, {'end': 754.213, 'text': 'And then the error is the magnitude of the difference between the truth and the answer given.', 'start': 748.01, 'duration': 6.203}, {'end': 758.375, 'text': 'And you can add all these up to get the total error for the neural network.', 'start': 755.254, 'duration': 3.121}, {'end': 770.45, 'text': 'So the idea, the whole idea with learning and training is to adjust the weights to make the error as low as possible.', 'start': 761.087, 'duration': 9.363}, {'end': 782.674, 'text': 'So the way this is done is we put an image in, we calculate the error at the end, then we look for how to adjust those weights higher or lower,', 'start': 771.93, 'duration': 10.744}, {'end': 785.495, 'text': 'to either make that error go up or down.', 'start': 782.674, 'duration': 2.821}, {'end': 789.376, 'text': 'And we of course adjust the weights in the way, then make the error go down.', 'start': 786.095, 'duration': 3.281}, {'end': 797.566, 'text': 'Now, the problem with doing this is, each time we go back and calculate the error,', 'start': 791.363, 'duration': 6.203}, {'end': 803.469, 'text': 'we have to multiply all of those weights by all of the neuron values at each layer.', 'start': 797.566, 'duration': 5.903}, {'end': 806.971, 'text': 'And we have to do that again and again once for each weight.', 'start': 804.47, 'duration': 2.501}, {'end': 813.355, 'text': 'This takes forever in computing terms, on a computing scale.', 'start': 808.352, 'duration': 5.003}, {'end': 817.417, 'text': "And so it's not a practical way to train a big neural network.", 'start': 813.815, 'duration': 3.602}, {'end': 823.275, 'text': 'You can imagine, instead of just rolling down to the bottom of a simple valley,', 'start': 819.192, 'duration': 4.083}, {'end': 827.319, 'text': 'we have a very high dimensional valley and we have to find our way down.', 'start': 823.275, 'duration': 4.044}, {'end': 834.785, 'text': 'And because there are so many dimensions, one for each of these weights, that the computation just becomes prohibitively expensive.', 'start': 827.839, 'duration': 6.946}, {'end': 843.112, 'text': 'Luckily, there was an insight that lets us do this in a very reasonable time.', 'start': 837.247, 'duration': 5.865}], 'summary': 'Neural networks aim to minimize errors by adjusting weights based on input, truth, and calculated errors, but the computational complexity can be reduced with a practical insight.', 'duration': 153.442, 'max_score': 689.67, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM689670.jpg'}, {'end': 770.45, 'src': 'embed', 'start': 702.301, 'weight': 4, 'content': [{'end': 709.94, 'text': "And so when we finally get to the output, We can see they're all zero except for this horizontal, which is positive, and that's the answer.", 'start': 702.301, 'duration': 7.639}, {'end': 715.002, 'text': 'Our neural network said this is an image of a horizontal line.', 'start': 710.02, 'duration': 4.982}, {'end': 721.345, 'text': "Now, neural networks usually aren't that good, not that clean.", 'start': 717.884, 'duration': 3.461}, {'end': 733.564, 'text': "So there's a notion of with an input, what is truth? In this case, the truth is this has a zero for all of these values, but a one for horizontal.", 'start': 722.326, 'duration': 11.238}, {'end': 736.105, 'text': "It's not solid, it's not vertical, it's not diagonal.", 'start': 733.764, 'duration': 2.341}, {'end': 737.866, 'text': 'Yes, it is horizontal.', 'start': 736.706, 'duration': 1.16}, {'end': 744.329, 'text': 'An arbitrary neural network will give answers that are not exactly truth.', 'start': 739.707, 'duration': 4.622}, {'end': 746.47, 'text': 'It might be off by a little or a lot.', 'start': 744.649, 'duration': 1.821}, {'end': 754.213, 'text': 'And then the error is the magnitude of the difference between the truth and the answer given.', 'start': 748.01, 'duration': 6.203}, {'end': 758.375, 'text': 'And you can add all these up to get the total error for the neural network.', 'start': 755.254, 'duration': 3.121}, {'end': 770.45, 'text': 'So the idea, the whole idea with learning and training is to adjust the weights to make the error as low as possible.', 'start': 761.087, 'duration': 9.363}], 'summary': 'Neural network identifies image with 0s and 1 positive as a horizontal line; goal is to minimize error by adjusting weights.', 'duration': 68.149, 'max_score': 702.301, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM702301.jpg'}, {'end': 870.412, 'src': 'embed', 'start': 844.073, 'weight': 3, 'content': [{'end': 851.458, 'text': "And that's that if we're careful about how we design our neural network, we can calculate the slope directly, the gradient.", 'start': 844.073, 'duration': 7.385}, {'end': 859.484, 'text': 'We can figure out the direction that we need to adjust the weight without going all the way back through our neural network and recalculating.', 'start': 851.979, 'duration': 7.505}, {'end': 870.412, 'text': "So just to review, the slope that we're talking about is when we make a change in weight, the error will change a little bit.", 'start': 862.686, 'duration': 7.726}], 'summary': 'Careful neural network design allows direct slope calculation for weight adjustment.', 'duration': 26.339, 'max_score': 844.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM844073.jpg'}, {'end': 1140.052, 'src': 'embed', 'start': 1115.404, 'weight': 2, 'content': [{'end': 1123.867, 'text': 'This, computationally, is many, many times cheaper than what we had to do before of recalculating the error for the whole neural network,', 'start': 1115.404, 'duration': 8.463}, {'end': 1124.467, 'text': 'for every weight.', 'start': 1123.867, 'duration': 0.6}, {'end': 1134.15, 'text': "Now, in the neural network that we've created, there are several types of back propagation we have to do.", 'start': 1127.528, 'duration': 6.622}, {'end': 1135.971, 'text': 'There are several operations we have to do.', 'start': 1134.23, 'duration': 1.741}, {'end': 1140.052, 'text': 'For each one of those, we have to be able to calculate the slope.', 'start': 1136.351, 'duration': 3.701}], 'summary': 'Neural network back propagation is now many times cheaper computationally.', 'duration': 24.648, 'max_score': 1115.404, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM1115404.jpg'}, {'end': 1352.103, 'src': 'embed', 'start': 1331.315, 'weight': 1, 'content': [{'end': 1342.115, 'text': 'Then We run it through, calculate the error, and using back propagation, go through and adjust all of those weights a tiny bit in the right direction.', 'start': 1331.315, 'duration': 10.8}, {'end': 1352.103, 'text': 'And then we do that again with another input and again with another input for, if we can get away with it, many thousands or even millions of times.', 'start': 1343.616, 'duration': 8.487}], 'summary': 'Adjust weights through backpropagation for thousands of inputs', 'duration': 20.788, 'max_score': 1331.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM1331315.jpg'}, {'end': 1443.385, 'src': 'embed', 'start': 1416.133, 'weight': 15, 'content': [{'end': 1423.22, 'text': "I haven't told you quite enough to be able to go out and build one of your own, but if you're feeling motivated to do so, I highly encourage it.", 'start': 1416.133, 'duration': 7.087}, {'end': 1426.723, 'text': "Here are a few resources that you'll find useful.", 'start': 1424.181, 'duration': 2.542}, {'end': 1429.686, 'text': "You'll want to go and learn about bias neurons.", 'start': 1427.584, 'duration': 2.102}, {'end': 1432.649, 'text': 'Dropout is a useful training tool.', 'start': 1430.827, 'duration': 1.822}, {'end': 1443.385, 'text': 'There are several resources available from Andrej Karpathy, who is an expert in neural networks and great at teaching about it.', 'start': 1434.002, 'duration': 9.383}], 'summary': 'Encourages building neural networks, recommends learning about bias neurons and dropout, and suggests resources from expert andrej karpathy.', 'duration': 27.252, 'max_score': 1416.133, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM1416133.jpg'}], 'start': 53.945, 'title': 'Neural networks fundamentals', 'summary': 'Covers neural network basics, layers and receptive fields, image recognition, and backpropagation, with emphasis on key concepts and practical examples, aiming to provide a comprehensive understanding for further learning.', 'chapters': [{'end': 332.238, 'start': 53.945, 'title': 'Neural network basics', 'summary': "Explains the basics of a neural network, including input neurons, weighted connections, and the sigmoid squashing function, which ensures that the neuron's value never gets outside the range of plus one to minus one.", 'duration': 278.293, 'highlights': ['The input neurons are associated with the brightness or darkness of each pixel, with plus one for all the way white, minus one for all the way black, and zero for gray.', 'The receptive field of a neuron determines the set of inputs that make the value of the neuron as high as possible.', 'The weighted connections in a neural network are multiplied by a number, and the final value is obtained after the weighted input neurons are added.', "The sigmoid squashing function ensures that the neuron's value never exceeds the range of plus one to minus one, keeping the computations in the neural network bounded and stable."]}, {'end': 554.771, 'start': 333.206, 'title': 'Neural network layers and receptive fields', 'summary': 'Discusses the creation of neural network layers and their receptive fields with weights between -1 and +1, highlighting the process of creating complex receptive fields through multiple layers and the use of rectified linear units for computation.', 'duration': 221.565, 'highlights': ['The process of creating complex receptive fields through multiple layers', 'The use of rectified linear units for computation', 'The receptive fields and their opposites created through rectified linear units']}, {'end': 746.47, 'start': 557.152, 'title': 'Neural network image recognition', 'summary': 'Explains how a neural network processes input images through multiple layers with neurons, and ultimately produces an output indicating the recognized image features, with an example showing the process and the final output accuracy.', 'duration': 189.318, 'highlights': ['The output layer of the neural network indicates the recognized image features, with a positive value for the horizontal line, providing the accurate answer.', "The concept of 'truth' in neural networks is discussed, with the recognition that arbitrary neural networks may produce answers that are not entirely accurate.", 'The process of combining input pixels in different layers with varying weights and receptive fields is detailed, showing the transformation and propagation of values through the network.']}, {'end': 1187.569, 'start': 748.01, 'title': 'Backpropagation in neural networks', 'summary': 'Explains the concept of backpropagation in neural networks, highlighting the need to minimize error by adjusting weights, the computational challenges in traditional methods, and the insight of calculating the gradient to efficiently adjust weights, using derivatives and chaining to calculate slopes, making the process computationally cheaper.', 'duration': 439.559, 'highlights': ['The insight of calculating the gradient directly allows efficient adjustment of weights, making the process computationally cheaper.', 'Chaining is used to calculate the slope of each tiny step and then multiply all of those together to get the slope of the full chain, making the process computationally cheaper.', 'The need to adjust weights to minimize error in the neural network is emphasized, with the goal of making the error as low as possible.', 'The computational challenges in traditional methods of adjusting weights for neural networks are highlighted, indicating the prohibitive expense and impracticality of the approach.', 'The concept of taking derivatives and using them to calculate slopes in adjusting weights is explained, illustrating the mathematical basis for backpropagation.']}, {'end': 1458.571, 'start': 1187.849, 'title': 'Neural network principles', 'summary': 'Explains the basics of backpropagation, weight adjustment through backpropagation, and training neural networks with random weights to gravitate towards a low error, providing recommendations for further learning resources.', 'duration': 270.722, 'highlights': ['The backpropagation process involves taking the derivative of the endpoints with respect to the step being propagated, and for certain functions like the sigmoid and rectified linear unit, their derivatives can be calculated by straightforward methods, providing a clear understanding of how to adjust weights in response to errors.', 'Training neural networks involves starting with random weights, calculating errors, using backpropagation to adjust weights, and repeating this process for many thousands or even millions of times, resulting in the weights gravitating towards a low error, with the potential for intuitive receptive fields and a relatively sparse representation.', 'The chapter concludes by recommending further learning resources on bias neurons, dropout, and practical tips from experts in the field of neural networks, emphasizing the complexity and difficulty in interpreting neural networks.']}], 'duration': 1404.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM53945.jpg', 'highlights': ['The process of combining input pixels in different layers with varying weights and receptive fields is detailed, showing the transformation and propagation of values through the network.', 'Training neural networks involves starting with random weights, calculating errors, using backpropagation to adjust weights, and repeating this process for many thousands or even millions of times, resulting in the weights gravitating towards a low error, with the potential for intuitive receptive fields and a relatively sparse representation.', 'The concept of taking derivatives and using them to calculate slopes in adjusting weights is explained, illustrating the mathematical basis for backpropagation.', 'The insight of calculating the gradient directly allows efficient adjustment of weights, making the process computationally cheaper.', 'The need to adjust weights to minimize error in the neural network is emphasized, with the goal of making the error as low as possible.', 'The backpropagation process involves taking the derivative of the endpoints with respect to the step being propagated, and for certain functions like the sigmoid and rectified linear unit, their derivatives can be calculated by straightforward methods, providing a clear understanding of how to adjust weights in response to errors.', 'The output layer of the neural network indicates the recognized image features, with a positive value for the horizontal line, providing the accurate answer.', 'The process of creating complex receptive fields through multiple layers', 'The use of rectified linear units for computation', 'The receptive fields and their opposites created through rectified linear units', "The concept of 'truth' in neural networks is discussed, with the recognition that arbitrary neural networks may produce answers that are not entirely accurate.", 'The weighted connections in a neural network are multiplied by a number, and the final value is obtained after the weighted input neurons are added.', 'The receptive field of a neuron determines the set of inputs that make the value of the neuron as high as possible.', 'The input neurons are associated with the brightness or darkness of each pixel, with plus one for all the way white, minus one for all the way black, and zero for gray.', "The sigmoid squashing function ensures that the neuron's value never exceeds the range of plus one to minus one, keeping the computations in the neural network bounded and stable.", 'The chapter concludes by recommending further learning resources on bias neurons, dropout, and practical tips from experts in the field of neural networks, emphasizing the complexity and difficulty in interpreting neural networks.']}, {'end': 3199.305, 'segs': [{'end': 1504.831, 'src': 'embed', 'start': 1477.647, 'weight': 4, 'content': [{'end': 1482.708, 'text': "In fact, we can even see how it's related to the most iconic model of all, linear regression.", 'start': 1477.647, 'duration': 5.061}, {'end': 1491.691, 'text': 'Simple linear regression assumes a straight line relationship between an input variable, x, and an output variable, y.', 'start': 1484.589, 'duration': 7.102}, {'end': 1502.069, 'text': "X is multiplied by a constant m, which also happens to be the slope of the line, and it's added to another constant b,", 'start': 1493.084, 'duration': 8.985}, {'end': 1504.831, 'text': 'which happens to be where the line crosses the y-axis.', 'start': 1502.069, 'duration': 2.762}], 'summary': 'Simple linear regression relates input variable x to output variable y via a straight line with slope m and intercept b.', 'duration': 27.184, 'max_score': 1477.647, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM1477647.jpg'}, {'end': 2310.096, 'src': 'embed', 'start': 2283.264, 'weight': 3, 'content': [{'end': 2287.026, 'text': 'using a line or a plane or a hyperplane of the appropriate dimensions.', 'start': 2283.264, 'duration': 3.762}, {'end': 2292.629, 'text': 'Another popular nonlinear function is the hyperbolic tangent.', 'start': 2289.367, 'duration': 3.262}, {'end': 2298.833, 'text': "It's closely related to the logistic function and can be written in a very symmetric way.", 'start': 2294.37, 'duration': 4.463}, {'end': 2310.096, 'text': 'We can see, when we choose some random weights and look at examples, that hyperbolic tangent curves look just like logistic curves,', 'start': 2300.748, 'duration': 9.348}], 'summary': 'Nonlinear functions like hyperbolic tangent closely related to logistic function', 'duration': 26.832, 'max_score': 2283.264, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM2283264.jpg'}, {'end': 2625.97, 'src': 'embed', 'start': 2600.41, 'weight': 2, 'content': [{'end': 2607.355, 'text': "Here's a generic diagram then for a three layer, single input, single output network.", 'start': 2600.41, 'duration': 6.945}, {'end': 2616.542, 'text': 'Notice that if we specify the number of inputs, the number of outputs and the number of layers, and the number of hidden nodes in each layer,', 'start': 2609.097, 'duration': 7.445}, {'end': 2619.244, 'text': 'then we can fully define a neural network.', 'start': 2616.542, 'duration': 2.702}, {'end': 2625.97, 'text': 'We can also take a look at a two input, single output neural network.', 'start': 2621.806, 'duration': 4.164}], 'summary': 'Generic diagram for a 3-layer single input, single output network.', 'duration': 25.56, 'max_score': 2600.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM2600410.jpg'}, {'end': 3062.317, 'src': 'embed', 'start': 3037.626, 'weight': 1, 'content': [{'end': 3044.469, 'text': "Some activation functions limit the output range, but as long as that matches the expected range of your outputs, it's not a problem.", 'start': 3037.626, 'duration': 6.843}, {'end': 3050.012, 'text': 'Second, neural networks are most often used for classification.', 'start': 3046.89, 'duration': 3.122}, {'end': 3051.872, 'text': "They've proven pretty good at it.", 'start': 3050.712, 'duration': 1.16}, {'end': 3062.317, 'text': 'Third, neural networks tend to create smooth functions when used for regression and smooth category boundaries when used for classification.', 'start': 3053.653, 'duration': 8.664}], 'summary': 'Neural networks: good for classification, smooth regression functions.', 'duration': 24.691, 'max_score': 3037.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM3037626.jpg'}, {'end': 3199.305, 'src': 'embed', 'start': 3168.972, 'weight': 0, 'content': [{'end': 3174.277, 'text': 'CNN can learn to play video games, in some cases, far better than a human ever could.', 'start': 3168.972, 'duration': 5.305}, {'end': 3188.282, 'text': 'Not only that, if you take a couple of CNNs and have them set to watching YouTube videos, one can learn objects by again picking out patterns,', 'start': 3177.079, 'duration': 11.203}, {'end': 3190.783, 'text': 'and the other one can learn types of grasps.', 'start': 3188.282, 'duration': 2.501}, {'end': 3199.305, 'text': 'This then, coupled with some other execution software, can let a robot learn to cook just by watching YouTube.', 'start': 3192.103, 'duration': 7.202}], 'summary': 'Cnn can outperform humans in video games and enable robots to learn cooking from youtube.', 'duration': 30.333, 'max_score': 3168.972, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM3168972.jpg'}], 'start': 1459.411, 'title': 'Neural network fundamentals and applications', 'summary': 'Discusses the relationship between linear regression and neural networks, the concept of linear network models and nonlinear functions, and the use of multilayer perceptrons in creating complex category boundaries. it also emphasizes the importance of normalized inputs and the learning capabilities of convolutional neural networks.', 'chapters': [{'end': 1642.574, 'start': 1459.411, 'title': 'Understanding neural networks', 'summary': 'Explains the relationship between linear regression and neural networks, illustrating how the former can be translated into the notation used in the latter, providing a graphical representation and explanation of the key concepts and terminology.', 'duration': 183.163, 'highlights': ['The chapter discusses the relationship between linear regression and neural networks, providing a graphical representation of the equation y equals mx plus b, and how it can be translated into the notation used in neural networks.', 'It explains the key concepts and terminology, such as nodes, edges, directed acyclic graph (DAG), and the formal definition of a network.', 'The chapter also emphasizes the importance of understanding what neural networks are actually learning when being trained, aiming to gain a clearer insight into their operations.']}, {'end': 2449.078, 'start': 1643.695, 'title': 'Linear network models and nonlinear functions', 'summary': 'Explains the linear network models, the addition of multiple inputs and layers, the introduction of non-linear functions like logistic and hyperbolic tangent, and the comparison of curves produced by single-layer and multi-layer networks.', 'duration': 805.383, 'highlights': ['The chapter explains the linear network models and the addition of multiple inputs and layers', 'Introduction of non-linear functions like logistic and hyperbolic tangent', 'Comparison of curves produced by single-layer and multi-layer networks']}, {'end': 3199.305, 'start': 2451.58, 'title': 'Neural networks and classifiers', 'summary': 'Explores the use of multilayer perceptrons in neural networks, their ability to create complex category boundaries, and the limitations and preferences of neural networks in regression and classification, emphasizing the importance of normalized inputs and the learning capabilities of convolutional neural networks.', 'duration': 747.725, 'highlights': ['Neural networks can create complex category boundaries and smooth functions, suitable for regression and classification tasks.', 'The limitations and preferences of neural networks in regression and classification are outlined, emphasizing the importance of normalized inputs.', 'Convolutional neural networks can learn to recognize patterns and objects, and even play video games and perform tasks like cooking by watching YouTube videos.']}], 'duration': 1739.894, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM1459411.jpg', 'highlights': ['Convolutional neural networks can learn to recognize patterns and objects, and even perform tasks like cooking by watching YouTube videos.', 'Neural networks can create complex category boundaries and smooth functions, suitable for regression and classification tasks.', 'The chapter explains the linear network models and the addition of multiple inputs and layers.', 'Introduction of non-linear functions like logistic and hyperbolic tangent.', 'The chapter discusses the relationship between linear regression and neural networks, providing a graphical representation of the equation y equals mx plus b.']}, {'end': 4272.245, 'segs': [{'end': 3260.587, 'src': 'embed', 'start': 3230.318, 'weight': 0, 'content': [{'end': 3234.822, 'text': 'You can think of it as a checkerboard, and each square on the checkerboard is either light or dark.', 'start': 3230.318, 'duration': 4.504}, {'end': 3244.117, 'text': "And then by looking at that, the CNN decides whether it's a picture of an X or of an O.", 'start': 3236.752, 'duration': 7.365}, {'end': 3251.741, 'text': 'So for instance, on top there, we see an image with an X drawn in white pixels on a black background.', 'start': 3244.117, 'duration': 7.624}, {'end': 3255.984, 'text': 'And we would like to identify this as an X.', 'start': 3252.402, 'duration': 3.582}, {'end': 3260.587, 'text': "And the O, we'd like to identify as an O.", 'start': 3255.984, 'duration': 4.603}], 'summary': 'A cnn processes images to identify x and o shapes.', 'duration': 30.269, 'max_score': 3230.318, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM3230318.jpg'}, {'end': 3810.018, 'src': 'embed', 'start': 3778.131, 'weight': 1, 'content': [{'end': 3785.693, 'text': "And the way this plays out is that if you're looking for a particular feature in an image, it can be a little to the left, a little to the right,", 'start': 3778.131, 'duration': 7.562}, {'end': 3788.633, 'text': "maybe a little rotated, and it'll still get picked up.", 'start': 3785.693, 'duration': 2.94}, {'end': 3801.296, 'text': 'So we do max pooling with all of our stack of filtered images and get, in every case, smaller set of filtered images.', 'start': 3792.374, 'duration': 8.922}, {'end': 3807.396, 'text': "Now, That's our second trick.", 'start': 3805.877, 'duration': 1.519}, {'end': 3810.018, 'text': 'Third trick, normalization.', 'start': 3807.877, 'duration': 2.141}], 'summary': 'Max pooling reduces filtered images to smaller sets, aiding in feature detection.', 'duration': 31.887, 'max_score': 3778.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM3778131.jpg'}, {'end': 3967.707, 'src': 'embed', 'start': 3941.004, 'weight': 4, 'content': [{'end': 3946.349, 'text': 'Now the final layer in our toolbox is called a fully connected layer.', 'start': 3941.004, 'duration': 5.345}, {'end': 3953.054, 'text': "Here, every value gets a vote on what the answer's going to be.", 'start': 3947.67, 'duration': 5.384}, {'end': 3958.679, 'text': 'So we take our now much filtered and much reduced in size stack of images.', 'start': 3954.135, 'duration': 4.544}, {'end': 3963.103, 'text': "we break them out, we just rearrange and put them into a single list, because it's easier to visualize that way.", 'start': 3958.679, 'duration': 4.424}, {'end': 3967.707, 'text': "And then each of those connects to one of our answers that we're gonna vote for.", 'start': 3963.944, 'duration': 3.763}], 'summary': 'Introduction to fully connected layer in image processing', 'duration': 26.703, 'max_score': 3941.004, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM3941004.jpg'}, {'end': 4141.716, 'src': 'embed', 'start': 4113.645, 'weight': 2, 'content': [{'end': 4126.92, 'text': 'Things that I pulled out of thin air include the features in the convolutional layers, those convenient 3 pixel by 3 pixel diagonal lines of the X.', 'start': 4113.645, 'duration': 13.275}, {'end': 4130.564, 'text': 'also the voting weights in the fully connected layers.', 'start': 4126.92, 'duration': 3.644}, {'end': 4133.207, 'text': 'I really waved my hands about how those are obtained.', 'start': 4130.743, 'duration': 2.464}, {'end': 4136.434, 'text': 'In all these cases, the answer is the same.', 'start': 4134.813, 'duration': 1.621}, {'end': 4139.555, 'text': 'There is a trick called backpropagation.', 'start': 4136.734, 'duration': 2.821}, {'end': 4141.716, 'text': 'All of these are learned.', 'start': 4140.316, 'duration': 1.4}], 'summary': 'Backpropagation is used to learn features and weights in convolutional and fully connected layers.', 'duration': 28.071, 'max_score': 4113.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4113645.jpg'}, {'end': 4229.136, 'src': 'embed', 'start': 4202.194, 'weight': 3, 'content': [{'end': 4210.601, 'text': 'If there is another bit of something that is pretty special sauce to deep neural networks, it is the ability to do gradient descent.', 'start': 4202.194, 'duration': 8.407}, {'end': 4217.126, 'text': 'So, for each of these magic numbers, each of the feature pixels, each voting weight,', 'start': 4211.962, 'duration': 5.164}, {'end': 4222.53, 'text': "they're adjusted up and down by a very small amount to see how the error changes.", 'start': 4217.126, 'duration': 5.404}, {'end': 4229.136, 'text': "The amount that they're adjusted is determined by how big the error is.", 'start': 4224.773, 'duration': 4.363}], 'summary': 'Deep neural networks use gradient descent to adjust feature pixels and voting weights for error minimization.', 'duration': 26.942, 'max_score': 4202.194, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4202194.jpg'}], 'start': 3202.205, 'title': 'Convolutional neural networks and computer vision basics', 'summary': 'Introduces cnns and their functionality by identifying xs and os in images, explaining the process of filtering, convolution, and pooling, and discusses max pooling, normalization, fully connected layers, backpropagation, and gradient descent for learning magic numbers in cnns.', 'chapters': [{'end': 3285.499, 'start': 3202.205, 'title': 'Understanding cnns', 'summary': 'Introduces the concept of convolutional neural networks (cnns), explaining their functionality through the example of identifying xs and os in images, highlighting the ability to recognize variations in size, rotation, and thickness.', 'duration': 83.294, 'highlights': ['The CNN is explained using a simple example of identifying Xs and Os in images, demonstrating its ability to recognize variations in size, rotation, and thickness.', 'CNNs are powerful tools based on basic ideas applied cleverly, not reliant on magical properties.', "The CNN takes in an image represented as a two-dimensional array of pixels, which is then used to identify whether it's a picture of an X or an O."]}, {'end': 3671.485, 'start': 3285.499, 'title': 'Computer vision basics', 'summary': 'Discusses how computers perceive images, explaining how convolutional neural networks use features to match smaller parts of an image, detailing the process of filtering and convolution to create a stack of filtered images, and introducing the concept of pooling to shrink the image stack.', 'duration': 385.986, 'highlights': ['Convolutional neural networks use features to match smaller parts of an image, making it clearer whether the two images are similar.', 'The process of filtering and convolution involves multiplying and adding up pixels, creating a map of where the feature occurs in the image.', 'Convolving an image with filters creates a stack of filtered images, known as a convolution layer.', 'Pooling is a technique used to shrink the image stack in convolutional neural networks.']}, {'end': 4272.245, 'start': 3672.146, 'title': 'Convolutional neural networks', 'summary': 'Explains the process of max pooling, normalization, and fully connected layers in convolutional neural networks, along with the concept of backpropagation and gradient descent for learning the magic numbers, ultimately leading to categorizing images based on votes and minimizing errors.', 'duration': 600.099, 'highlights': ['The process of max pooling reduces the size of the filtered image by taking the maximum value within a window and moving it in strides, resulting in a smaller but similar pattern.', 'Normalization involves changing all negative values to zero in the filtered images, creating a stack of images with no negative values, helping to prevent the math from blowing up.', 'The fully connected layer in convolutional neural networks arranges the filtered and reduced stack of images into a single list, where each value gets a vote for the predicted category, enabling the categorization of images based on votes and strengths.', 'Backpropagation is a learning trick behind obtaining the feature pixels in convolutional layers, voting weights in fully connected layers, and the error-driven adjustment of the deep neural network, ensuring that the network learns these values without manual intervention.', 'Gradient descent, a special sauce to deep neural networks, involves adjusting the feature pixels and voting weights based on the error, by sliding them slightly and finding the downhill direction to minimize errors, ultimately leading to the least error as the happy place.']}], 'duration': 1070.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM3202205.jpg', 'highlights': ["The CNN takes in an image represented as a two-dimensional array of pixels, which is then used to identify whether it's a picture of an X or an O.", 'The process of max pooling reduces the size of the filtered image by taking the maximum value within a window and moving it in strides, resulting in a smaller but similar pattern.', 'Backpropagation is a learning trick behind obtaining the feature pixels in convolutional layers, voting weights in fully connected layers, and the error-driven adjustment of the deep neural network, ensuring that the network learns these values without manual intervention.', 'Gradient descent, a special sauce to deep neural networks, involves adjusting the feature pixels and voting weights based on the error, by sliding them slightly and finding the downhill direction to minimize errors, ultimately leading to the least error as the happy place.', 'The fully connected layer in convolutional neural networks arranges the filtered and reduced stack of images into a single list, where each value gets a vote for the predicted category, enabling the categorization of images based on votes and strengths.']}, {'end': 4871.524, 'segs': [{'end': 4300.539, 'src': 'embed', 'start': 4272.245, 'weight': 0, 'content': [{'end': 4280.589, 'text': "helps all of these values across all the features and all of the weights settle in to what's called a minimum.", 'start': 4272.245, 'duration': 8.344}, {'end': 4285.852, 'text': 'And at that point, the network is performing as well as it possibly can.', 'start': 4282.03, 'duration': 3.822}, {'end': 4289.534, 'text': 'If it adjusts any of those a little bit, its error will go up.', 'start': 4285.872, 'duration': 3.662}, {'end': 4300.539, 'text': 'Now, there are some things called hyperparameters, and these are knobs that the designer gets to turn, decisions the designer gets to make.', 'start': 4292.215, 'duration': 8.324}], 'summary': 'Neural network achieves peak performance when weights reach minimum, adjusting hyperparameters impacts performance.', 'duration': 28.294, 'max_score': 4272.245, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4272245.jpg'}, {'end': 4361.792, 'src': 'embed', 'start': 4331.536, 'weight': 1, 'content': [{'end': 4332.557, 'text': 'Right now.', 'start': 4331.536, 'duration': 1.021}, {'end': 4339.72, 'text': 'there are some common practices that tend to work better than others, but there is no principled way.', 'start': 4332.557, 'duration': 7.163}, {'end': 4342.281, 'text': "there's no hard and fast rules for the right way to do this.", 'start': 4339.72, 'duration': 2.561}, {'end': 4351.086, 'text': 'And in fact, a lot of the advances in convolutional neural networks are in getting combinations of these that work really well.', 'start': 4343.442, 'duration': 7.644}, {'end': 4359.97, 'text': 'Now, in addition to this, there are other decisions the designer gets to make, like how many of each type of layer.', 'start': 4353.447, 'duration': 6.523}, {'end': 4361.792, 'text': 'And in what order?', 'start': 4360.891, 'duration': 0.901}], 'summary': 'Common practices vary for convolutional neural networks, no strict rules, advances in getting effective combinations, designer decisions include layer quantity and order.', 'duration': 30.256, 'max_score': 4331.536, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4331536.jpg'}, {'end': 4654.802, 'src': 'embed', 'start': 4603.084, 'weight': 2, 'content': [{'end': 4609.946, 'text': 'So the take home is that convolutional neural networks are great at finding patterns and using them to classify images.', 'start': 4603.084, 'duration': 6.862}, {'end': 4618.428, 'text': "If you can make your problem look like finding cats on the internet, then they're a huge asset.", 'start': 4611.146, 'duration': 7.282}, {'end': 4623.956, 'text': 'Applications of machine learning have gotten a lot of traction in the last few years.', 'start': 4619.372, 'duration': 4.584}, {'end': 4627.199, 'text': "There's a couple of big categories that have had wins.", 'start': 4624.837, 'duration': 2.362}, {'end': 4636.087, 'text': 'One is identifying pictures, the equivalent of finding cats on the internet, and any problem that can be made to look like that.', 'start': 4627.82, 'duration': 8.267}, {'end': 4640.632, 'text': 'And the other is sequence-to-sequence translation.', 'start': 4636.708, 'duration': 3.924}, {'end': 4643.855, 'text': 'This can be speech-to-text or one language to another.', 'start': 4640.892, 'duration': 2.963}, {'end': 4647.937, 'text': 'Most of the former are done with convolutional neural networks.', 'start': 4644.835, 'duration': 3.102}, {'end': 4654.802, 'text': 'Most of the latter are done with recurrent neural networks, particularly long short-term memory.', 'start': 4648.598, 'duration': 6.204}], 'summary': 'Convolutional neural networks excel at image recognition, with applications in identifying pictures and sequence-to-sequence translation.', 'duration': 51.718, 'max_score': 4603.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4603084.jpg'}], 'start': 4272.245, 'title': 'Hyperparameters and applications of convolutional neural networks', 'summary': 'Explains the role of hyperparameters in neural networks, emphasizing the absence of hard and fast rules for setting them and discusses the versatility of convolutional neural networks, highlighting their applications in processing various types of data and pattern recognition.', 'chapters': [{'end': 4371.058, 'start': 4272.245, 'title': 'Hyperparameters in neural networks', 'summary': "Explains the role of hyperparameters in neural networks, highlighting that these are manually set values that influence the network's performance and the absence of hard and fast rules for setting them. it also mentions that a lot of the advances in convolutional neural networks are in getting combinations of these hyperparameters that work really well.", 'duration': 98.813, 'highlights': ["The role of hyperparameters in neural networks, as manually set values that influence the network's performance, with no principled way or hard and fast rules for setting them.", 'The mention of common practices in setting hyperparameters, but the absence of a principled way or hard and fast rules for determining the right combinations of hyperparameters.', 'The advances in convolutional neural networks are in finding combinations of hyperparameters that work really well, indicating the significance of setting hyperparameters for network performance.']}, {'end': 4871.524, 'start': 4372.079, 'title': 'Applications of convolutional neural networks', 'summary': 'Discusses the versatility of convolutional neural networks, highlighting their applications in processing various types of data, such as sound and text, and emphasizing their effectiveness in pattern recognition and image classification.', 'duration': 499.445, 'highlights': ['The versatility of convolutional neural networks in processing different types of data', 'Effectiveness of CNNs in capturing local spatial patterns and classifying images', 'Applications of CNNs in identifying pictures and sequence-to-sequence translation']}], 'duration': 599.279, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4272245.jpg', 'highlights': ["The role of hyperparameters in neural networks, as manually set values that influence the network's performance, with no principled way or hard and fast rules for setting them.", 'The mention of common practices in setting hyperparameters, but the absence of a principled way or hard and fast rules for determining the right combinations of hyperparameters.', 'The advances in convolutional neural networks are in finding combinations of hyperparameters that work really well, indicating the significance of setting hyperparameters for network performance.', 'The versatility of convolutional neural networks in processing different types of data', 'Effectiveness of CNNs in capturing local spatial patterns and classifying images', 'Applications of CNNs in identifying pictures and sequence-to-sequence translation']}, {'end': 6184.375, 'segs': [{'end': 4946.132, 'src': 'embed', 'start': 4912.382, 'weight': 0, 'content': [{'end': 4916.885, 'text': "And it's very common to see a long vector of zeros with just one element being one.", 'start': 4912.382, 'duration': 4.503}, {'end': 4922.944, 'text': 'It seems inefficient, but for a computer, this is a lot easier way to ingest that information.', 'start': 4917.802, 'duration': 5.142}, {'end': 4929.606, 'text': 'So we can make a one hot vector for our prediction for dinner tonight.', 'start': 4925.244, 'duration': 4.362}, {'end': 4934.267, 'text': 'We set everything equal to zero except for the dinner item that we predict.', 'start': 4930.366, 'duration': 3.901}, {'end': 4936.888, 'text': "So in this case, we'll be predicting sushi.", 'start': 4934.867, 'duration': 2.021}, {'end': 4946.132, 'text': 'Now we can group together our inputs and outputs into vectors,', 'start': 4939.269, 'duration': 6.863}], 'summary': 'Using one hot vectors for efficient computer processing and predicting sushi for dinner.', 'duration': 33.75, 'max_score': 4912.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4912382.jpg'}, {'end': 5064.279, 'src': 'embed', 'start': 5032.111, 'weight': 2, 'content': [{'end': 5036.172, 'text': "It'll have sentences of the format, Doug saw Jane, period.", 'start': 5032.111, 'duration': 4.061}, {'end': 5038.813, 'text': 'Jane saw Spot, period.', 'start': 5036.792, 'duration': 2.021}, {'end': 5041.194, 'text': 'Spot saw Doug, period.', 'start': 5039.333, 'duration': 1.861}, {'end': 5042.334, 'text': 'And so on.', 'start': 5041.794, 'duration': 0.54}, {'end': 5046.155, 'text': 'So our dictionary is small.', 'start': 5044.735, 'duration': 1.42}, {'end': 5051.637, 'text': 'Just the words Doug, Jane, Spot, saw, and a period.', 'start': 5046.635, 'duration': 5.002}, {'end': 5057.654, 'text': "And the task of the neural network is to put these together in the right order to make a good children's book.", 'start': 5052.93, 'duration': 4.724}, {'end': 5064.279, 'text': 'So to do this, we replace our food vectors with our dictionary vectors.', 'start': 5059.075, 'duration': 5.204}], 'summary': "Neural network tasked with ordering words to create children's book.", 'duration': 32.168, 'max_score': 5032.111, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM5032111.jpg'}, {'end': 5318.507, 'src': 'embed', 'start': 5287.298, 'weight': 1, 'content': [{'end': 5292.819, 'text': "then it doesn't use the information from further back and it's subject to these types of mistakes.", 'start': 5287.298, 'duration': 5.521}, {'end': 5300.541, 'text': 'In order to overcome this, we take our recurrent neural network and we expand it and we add some more pieces to it.', 'start': 5294.52, 'duration': 6.021}, {'end': 5306.243, 'text': 'The critical part that we add to the middle here is memory.', 'start': 5302.422, 'duration': 3.821}, {'end': 5311.284, 'text': 'We want to be able to remember what happened many time steps ago.', 'start': 5307.283, 'duration': 4.001}, {'end': 5318.507, 'text': "So in order to explain how this works, I'll have to describe a few new symbols that we've introduced here.", 'start': 5313.482, 'duration': 5.025}], 'summary': 'Expanding recurrent neural network with memory to remember past events.', 'duration': 31.209, 'max_score': 5287.298, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM5287298.jpg'}, {'end': 5400.835, 'src': 'embed', 'start': 5371.581, 'weight': 3, 'content': [{'end': 5378.384, 'text': "And very closely related to this, you've probably guessed, the X in the circle is element by element multiplication.", 'start': 5371.581, 'duration': 6.803}, {'end': 5382.446, 'text': "It's just like addition, except instead of adding, you multiply.", 'start': 5379.445, 'duration': 3.001}, {'end': 5387.749, 'text': 'For instance, three times six gives you a first element of 18.', 'start': 5382.926, 'duration': 4.823}, {'end': 5390.25, 'text': 'Four times seven gets you 28.', 'start': 5387.749, 'duration': 2.501}, {'end': 5394.112, 'text': 'Again, the output vector is the same size of each of the input vectors.', 'start': 5390.25, 'duration': 3.862}, {'end': 5400.835, 'text': 'Now, element-wise multiplication lets you do something pretty cool.', 'start': 5397.433, 'duration': 3.402}], 'summary': 'Element-wise multiplication performs operations on vectors, e.g. 3*6=18, 4*7=28.', 'duration': 29.254, 'max_score': 5371.581, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM5371581.jpg'}, {'end': 5501.859, 'src': 'embed', 'start': 5444.841, 'weight': 4, 'content': [{'end': 5452.966, 'text': 'That signal passed right through into the output vector, but the last element 0.8 times zero equals zero.', 'start': 5444.841, 'duration': 8.125}, {'end': 5456.748, 'text': 'That signal, the original signal was effectively blocked.', 'start': 5453.966, 'duration': 2.782}, {'end': 5463.311, 'text': "And then with the gating value of 0.5, the signal was passed through, but it's smaller.", 'start': 5458.049, 'duration': 5.262}, {'end': 5464.371, 'text': "It's attenuated.", 'start': 5463.671, 'duration': 0.7}, {'end': 5473.235, 'text': 'So gating lets us control what passes through and what gets blocked, which is really useful.', 'start': 5465.972, 'duration': 7.263}, {'end': 5482.003, 'text': "Now, in order to do gating, it's nice to have a value that you know is always between 0 and 1.", 'start': 5475.236, 'duration': 6.767}, {'end': 5484.685, 'text': 'So we introduce another squashing function.', 'start': 5482.003, 'duration': 2.682}, {'end': 5490.49, 'text': "This will represent with a circle with a flat bottom, and it's called the logistic function.", 'start': 5485.306, 'duration': 5.184}, {'end': 5501.859, 'text': "It's very similar to the other squashing function, the hyperbolic tangent, except that it just goes between 0 and 1 instead of minus 1 and 1.", 'start': 5490.97, 'duration': 10.889}], 'summary': 'Gating controls signal flow, with a value between 0 and 1, using a logistic function.', 'duration': 57.018, 'max_score': 5444.841, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM5444841.jpg'}, {'end': 6035.823, 'src': 'embed', 'start': 6006.092, 'weight': 6, 'content': [{'end': 6013.234, 'text': 'Now, to be fair to vanilla recurrent neural networks, they can actually look back several time steps as well, but not very many.', 'start': 6006.092, 'duration': 7.142}, {'end': 6019.396, 'text': 'LSTM can look back many time steps and has shown that successfully.', 'start': 6014.555, 'duration': 4.841}, {'end': 6027.298, 'text': 'This is really useful in some surprisingly practical applications.', 'start': 6022.415, 'duration': 4.883}, {'end': 6035.823, 'text': 'If I have text in one language and I want to translate it to text to another language, LSTMs work very well.', 'start': 6028.318, 'duration': 7.505}], 'summary': 'Lstms can look back many time steps and work well for language translation.', 'duration': 29.731, 'max_score': 6006.092, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM6006092.jpg'}, {'end': 6103.654, 'src': 'embed', 'start': 6077.618, 'weight': 7, 'content': [{'end': 6088.348, 'text': 'It takes them and uses that then to predict what text, what word is being spoken, and it can use the history, the recent history of words,', 'start': 6077.618, 'duration': 10.73}, {'end': 6090.649, 'text': "to make a better guess for what's gonna come next.", 'start': 6088.348, 'duration': 2.301}, {'end': 6097.956, 'text': "LSTMs are a great fit for any information that's embedded in time.", 'start': 6092.751, 'duration': 5.205}, {'end': 6100.572, 'text': 'audio, video.', 'start': 6098.971, 'duration': 1.601}, {'end': 6103.654, 'text': 'My favorite application of all, of course, is robotics.', 'start': 6101.412, 'duration': 2.242}], 'summary': 'Lstms predict text and are great for time-based data like audio, video, and robotics.', 'duration': 26.036, 'max_score': 6077.618, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM6077618.jpg'}], 'start': 4872.304, 'title': 'Neural network and lstm', 'summary': 'Covers neural network processing, including lstm networks, used in predicting and memory retention. it also explains element-wise operations, gating, and practical applications of lstms in language translation and speech recognition.', 'chapters': [{'end': 5311.284, 'start': 4872.304, 'title': 'Neural network and recurrent neural network', 'summary': "Explains the use of lists of numbers, one hot encoding, and recurrent neural networks in processing information and making predictions, with an example of predicting dinner items and creating a children's book, while addressing the limitations of short-term memory and introducing the concept of memory in recurrent neural networks.", 'duration': 438.98, 'highlights': ['The chapter emphasizes the use of lists of numbers and one hot encoding to represent information, reducing it to a format that is natural for a computer to process, with an example of creating a one hot vector for predicting dinner items.', 'The explanation of recurrent neural networks includes the concept of memory, addressing the limitation of short-term memory and the need to remember information from many time steps ago.', "The chapter introduces the task of creating a children's book using a neural network, replacing food vectors with dictionary vectors and teaching the network to put words in the right order, showcasing the application of neural networks in creative tasks."]}, {'end': 5501.859, 'start': 5313.482, 'title': 'Element-wise operations and gating in neural networks', 'summary': 'Explains element-wise addition and multiplication in neural networks, demonstrating how they are used to control signal flow and perform gating, while introducing the logistic function as a means of constraining values between 0 and 1.', 'duration': 188.377, 'highlights': ['The chapter details element-wise multiplication and addition, showcasing how they are used in neural networks to process vectors and produce output vectors of the same size as input vectors.', 'The concept of gating and its practical application in controlling signal flow is explained, with examples of how gating values between 0 and 1 can pass, attenuate, or block signals.', 'The introduction of the logistic function as a means of constraining values between 0 and 1 is discussed, highlighting its similarity to the hyperbolic tangent function but with a range limited to 0 and 1.']}, {'end': 6184.375, 'start': 5501.859, 'title': 'Understanding long short-term memory networks', 'summary': 'Provides an overview of long short-term memory (lstm) networks, describing their architecture and functionality, including the mechanisms of prediction, memory retention, forgetting, selection, and attention. it also highlights the practical applications of lstms, such as language translation and speech recognition, emphasizing their ability to handle sequential data effectively.', 'duration': 682.516, 'highlights': ['LSTM architecture and functionality', 'Practical applications of LSTMs', 'Capability of LSTMs in language translation and speech recognition']}], 'duration': 1312.071, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM4872304.jpg', 'highlights': ['The chapter emphasizes the use of lists of numbers and one hot encoding to represent information, reducing it to a format that is natural for a computer to process, with an example of creating a one hot vector for predicting dinner items.', 'The explanation of recurrent neural networks includes the concept of memory, addressing the limitation of short-term memory and the need to remember information from many time steps ago.', "The chapter introduces the task of creating a children's book using a neural network, replacing food vectors with dictionary vectors and teaching the network to put words in the right order, showcasing the application of neural networks in creative tasks.", 'The chapter details element-wise multiplication and addition, showcasing how they are used in neural networks to process vectors and produce output vectors of the same size as input vectors.', 'The concept of gating and its practical application in controlling signal flow is explained, with examples of how gating values between 0 and 1 can pass, attenuate, or block signals.', 'The introduction of the logistic function as a means of constraining values between 0 and 1 is discussed, highlighting its similarity to the hyperbolic tangent function but with a range limited to 0 and 1.', 'LSTM architecture and functionality', 'Practical applications of LSTMs', 'Capability of LSTMs in language translation and speech recognition']}, {'end': 7466.179, 'segs': [{'end': 6214.828, 'src': 'embed', 'start': 6185.395, 'weight': 0, 'content': [{'end': 6187.677, 'text': "It's hard to know what it can't do.", 'start': 6185.395, 'duration': 2.282}, {'end': 6189.398, 'text': "We don't get to talk about that very much.", 'start': 6188.017, 'duration': 1.381}, {'end': 6193.661, 'text': 'So the goal of this talk is just to talk about it on a really simple nuts and bolts level.', 'start': 6189.938, 'duration': 3.723}, {'end': 6201.966, 'text': "The summary, in case you want to take a nap, deep learning is not magic, but it's really good at finding patterns.", 'start': 6194.901, 'duration': 7.065}, {'end': 6209.384, 'text': 'So if this is our brain, this is deep learning.', 'start': 6203.959, 'duration': 5.425}, {'end': 6211.866, 'text': 'An owl can fly.', 'start': 6210.565, 'duration': 1.301}, {'end': 6214.828, 'text': 'A fighter jet can fly.', 'start': 6211.886, 'duration': 2.942}], 'summary': "Deep learning is not magic; it's great at finding patterns.", 'duration': 29.433, 'max_score': 6185.395, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM6185395.jpg'}, {'end': 6559.627, 'src': 'embed', 'start': 6527.246, 'weight': 1, 'content': [{'end': 6529.408, 'text': 'We can substitute in numbers for that.', 'start': 6527.246, 'duration': 2.162}, {'end': 6539.397, 'text': 'The weights, although most often, oh, we can also substitute in line thicknesses to show how strongly these things are connected.', 'start': 6530.373, 'duration': 9.024}, {'end': 6542.419, 'text': 'Most of the time, neural networks are drawn like this.', 'start': 6540.398, 'duration': 2.021}, {'end': 6544.76, 'text': 'And this is what we have.', 'start': 6543.519, 'duration': 1.241}, {'end': 6553.924, 'text': 'We went from the super complex slice of brain tissue, with many subtleties in its operation and interconnection,', 'start': 6545.32, 'duration': 8.604}, {'end': 6559.627, 'text': 'to a nice circle stick diagram where each one of those sticks represents a weight.', 'start': 6553.924, 'duration': 5.703}], 'summary': 'Neural networks simplify complex brain tissue into stick diagrams with weights.', 'duration': 32.381, 'max_score': 6527.246, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM6527246.jpg'}, {'end': 6842.409, 'src': 'embed', 'start': 6805.941, 'weight': 2, 'content': [{'end': 6807.402, 'text': 'We create our neural network.', 'start': 6805.941, 'duration': 1.461}, {'end': 6809.244, 'text': 'We have our input neurons.', 'start': 6808.183, 'duration': 1.061}, {'end': 6812.947, 'text': 'All we choose is the number of output neurons.', 'start': 6810.065, 'duration': 2.882}, {'end': 6816.73, 'text': "In this case, we'll choose two because we happen to know we're learning two patterns.", 'start': 6812.967, 'duration': 3.763}, {'end': 6819.313, 'text': 'And then we randomly assign weights.', 'start': 6817.451, 'duration': 1.862}, {'end': 6822.635, 'text': 'randomly generate numbers for each of these.', 'start': 6820.393, 'duration': 2.242}, {'end': 6829.179, 'text': "It's a neural network that's completely, you roll the dice, you throw the sticks, and whatever falls out, that's what you start with.", 'start': 6822.895, 'duration': 6.284}, {'end': 6833.282, 'text': 'And then we start to gather data.', 'start': 6832.221, 'duration': 1.061}, {'end': 6842.409, 'text': 'We go, stand on the other side of the street and we observe that the shawarma guy on this particular day worked in the morning and then went home,', 'start': 6834.183, 'duration': 8.226}], 'summary': 'Creating a neural network with 2 output neurons to learn 2 patterns, and randomly assigning weights for initial setup.', 'duration': 36.468, 'max_score': 6805.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM6805941.jpg'}, {'end': 7213.397, 'src': 'embed', 'start': 7181.157, 'weight': 3, 'content': [{'end': 7183.377, 'text': 'And you can do this as deep as you like.', 'start': 7181.157, 'duration': 2.22}, {'end': 7189.558, 'text': "So there's a variety of things you can learn with deep neural networks.", 'start': 7185.818, 'duration': 3.74}, {'end': 7191.719, 'text': 'A very popular one is images.', 'start': 7190.319, 'duration': 1.4}, {'end': 7201.766, 'text': "If you take as your inputs pixels and show, instead of looking at a shawarma guy's schedule,", 'start': 7193.497, 'duration': 8.269}, {'end': 7205.75, 'text': "you're looking at individual pictures as your training data set.", 'start': 7201.766, 'duration': 3.984}, {'end': 7213.397, 'text': 'what you start to learn after a while is these little representations of short lines and dots and patches.', 'start': 7205.75, 'duration': 7.647}], 'summary': 'Deep neural networks can learn to recognize images by analyzing pixels, identifying lines, dots, and patches.', 'duration': 32.24, 'max_score': 7181.157, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM7181157.jpg'}], 'start': 6185.395, 'title': 'Deep learning fundamentals', 'summary': 'Delves into deep learning basics, understanding neurons and neural networks, the neural network learning process, and the applications of deep learning in various fields, aiming to simplify complex principles and highlight potential capabilities.', 'chapters': [{'end': 6260.074, 'start': 6185.395, 'title': 'Understanding deep learning basics', 'summary': 'Explains that deep learning is not magic but excels at finding patterns, likening it to a highly specialized fighter jet in contrast to the diverse capabilities of an owl, aiming to simplify the understanding of its principles.', 'duration': 74.679, 'highlights': ['Deep learning is compared to a highly specialized fighter jet, excelling at specific tasks, whereas an owl represents a more diverse and complex capability.', 'The talk aims to simplify the understanding of deep learning by discussing it at a nuts and bolts level and likens it to the Wright Brothers airplane, emphasizing the importance of grasping its fundamental principles.', 'Deep learning is described as not being magical but proficient at discovering patterns, highlighting its practical and non-mystical nature.', 'The speaker encourages discussing deep learning at a comfortable level, making it accessible for understanding basic principles, similar to explaining the concept of a neuron.']}, {'end': 6728.362, 'start': 6261.054, 'title': 'Understanding neurons and neural networks', 'summary': 'Provides an overview of neurons and neural networks, explaining their structure and function, including the process of signal transmission, synapses, and the concept of connection strength, highlighting the potential applications and capabilities of neural networks in representing combinations of inputs.', 'duration': 467.308, 'highlights': ['Neurons have a body, long tails (axons), and branching arms (dendrites), with a dense packing and a high connectivity to their neighbors, showcasing the complexity and interconnections within neural networks.', 'The process of signal transmission in neurons involves dendrites picking up electrical activity and sending it to the soma, which accumulates and activates the axon to transmit the signal, demonstrating the fundamental mechanism of neural communication.', 'The strength of connection between neurons, represented as a number between 0 and 1, determines the activation of the output neuron, showing how varying connection strengths impact the overall network activity and output.', 'Neural networks can effectively represent combinations of inputs, as demonstrated by the examples of visual and text processing, showcasing the potential for neural networks to interpret and process different types of input data for various applications.']}, {'end': 7046.691, 'start': 6729.462, 'title': 'Neural network learning process', 'summary': 'Discusses the process of learning in a neural network, including the representation of working patterns with output neurons, automatic learning through data gathering, calculation of activity in output neurons, error calculation, and the concept of gradient descent for weight adjustment.', 'duration': 317.229, 'highlights': ['The network represents working patterns with output neurons combining inputs, with two patterns being learned and represented by the connection strengths.', 'Automatic learning in the neural network is achieved through the gathering of data, and the initial random assignment of weights, which is then adjusted based on observed data.', 'The activity of output neurons is calculated by taking the average of the inputs weighted by the connection strengths, with the error indicating the need for weight adjustment.', "The concept of gradient descent is employed to adjust weights by making small incremental changes to minimize error and improve the network's representation of observed data."]}, {'end': 7466.179, 'start': 7047.751, 'title': 'Deep learning and neural networks', 'summary': 'Explains the underlying mechanism of deep learning and neural networks, including the concept of deep neural networks, their applications in image recognition, music artist categorization, atari 2600 game playing, and robot learning from youtube videos, as well as the potential of deep learning in learning patterns and recognizing complex features.', 'duration': 418.428, 'highlights': ['Deep learning and neural networks, including the concept of deep neural networks, their applications in image recognition, music artist categorization, Atari 2600 game playing, and robot learning from YouTube videos.', 'Potential of deep learning in learning patterns and recognizing complex features.']}], 'duration': 1280.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM6185395.jpg', 'highlights': ['Deep learning simplifies complex principles, not magical but proficient at discovering patterns', 'Neural networks showcase complexity and interconnections, signal transmission and connection strengths', 'Neural networks represent working patterns, automatic learning through data gathering and weight adjustment', 'Deep learning and neural networks applications in image recognition, music artist categorization, and robot learning']}, {'end': 8187.068, 'segs': [{'end': 7527.253, 'src': 'embed', 'start': 7496.385, 'weight': 1, 'content': [{'end': 7501.488, 'text': 'Also, you could do many things, but do them all very poorly and also not be that intelligent.', 'start': 7496.385, 'duration': 5.103}, {'end': 7506.611, 'text': 'Intelligence is the combination of being able to do many things and to do them well.', 'start': 7502.729, 'duration': 3.882}, {'end': 7511.233, 'text': 'This is a functional definition of intelligence.', 'start': 7508.812, 'duration': 2.421}, {'end': 7514.875, 'text': 'There are many other potential definitions.', 'start': 7512.234, 'duration': 2.641}, {'end': 7517.977, 'text': "Some of them can be measured experimentally and some can't.", 'start': 7515.355, 'duration': 2.622}, {'end': 7527.253, 'text': 'This particular definition has the advantage that if we wanted to reduce it down to a measurable set of tasks, we could.', 'start': 7519.69, 'duration': 7.563}], 'summary': 'Intelligence is the ability to do many things well, a functional definition with potential for experimental measurement.', 'duration': 30.868, 'max_score': 7496.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM7496385.jpg'}, {'end': 7576.122, 'src': 'embed', 'start': 7545.52, 'weight': 0, 'content': [{'end': 7547.681, 'text': "In short, it's practical and useful.", 'start': 7545.52, 'duration': 2.161}, {'end': 7552.531, 'text': 'Undoubtedly, some will find it philosophically unsatisfying.', 'start': 7549.389, 'duration': 3.142}, {'end': 7557.093, 'text': "That's a separate conversation, but one that I would be happy to have in another forum.", 'start': 7553.411, 'duration': 3.682}, {'end': 7564.396, 'text': 'Using fake math, we can say that intelligence equals performance times generality.', 'start': 7559.854, 'duration': 4.542}, {'end': 7569.519, 'text': "It's only fake because we haven't defined performance or generality yet.", 'start': 7565.677, 'duration': 3.842}, {'end': 7576.122, 'text': 'But assuming we do, you can imagine plotting them and putting them on a set of axes like this.', 'start': 7570.339, 'duration': 5.783}], 'summary': 'Intelligence is performance times generality, but we lack defined metrics.', 'duration': 30.602, 'max_score': 7545.52, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM7545520.jpg'}, {'end': 7944.782, 'src': 'embed', 'start': 7914.257, 'weight': 3, 'content': [{'end': 7918.819, 'text': 'And now a chess program running on your phone can do about the same as Deep Blue did.', 'start': 7914.257, 'duration': 4.562}, {'end': 7931.109, 'text': 'The current state of the art is a program called Stockfish, which has an ELO rating, which is like a chess skill score of 3447.', 'start': 7920.54, 'duration': 10.569}, {'end': 7938.276, 'text': 'Compare this to the top rated human player of all time, Magnus Carlsen, who achieved a 2882.', 'start': 7931.109, 'duration': 7.167}, {'end': 7943.561, 'text': 'The program and the human are not even comparable.', 'start': 7938.276, 'duration': 5.285}, {'end': 7944.782, 'text': "They're not even close.", 'start': 7943.701, 'duration': 1.081}], 'summary': "Stockfish program has an elo rating of 3447, surpassing magnus carlsen's 2882.", 'duration': 30.525, 'max_score': 7914.257, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM7914257.jpg'}, {'end': 8163.919, 'src': 'embed', 'start': 8124.805, 'weight': 4, 'content': [{'end': 8133.207, 'text': 'So when doing a tree search, the number of moves explodes much more quickly than in chess.', 'start': 8124.805, 'duration': 8.402}, {'end': 8148.449, 'text': 'Now, despite this, two years ago already, AlphaGo, a program built by researchers at DeepMind, beat Lee Sedol, a professional 9dan player.', 'start': 8134.946, 'duration': 13.503}, {'end': 8155.39, 'text': 'Professional 9dan would put him in the all-stars of the Go world.', 'start': 8150.109, 'duration': 5.281}, {'end': 8158.451, 'text': 'A later version.', 'start': 8157.591, 'duration': 0.86}, {'end': 8163.919, 'text': 'AlphaGo Master was clocked with an ELO rating of 4858,.', 'start': 8158.451, 'duration': 5.468}], 'summary': 'In go, alphago with an elo rating of 4858 beat a professional 9dan player.', 'duration': 39.114, 'max_score': 8124.805, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM8124805.jpg'}], 'start': 7467.93, 'title': 'Defining intelligence', 'summary': 'Discusses a functional definition of intelligence as the combination of performance and generality, enabling comparison of relative intelligence. it also examines human vs machine intelligence, highlighting specific examples of machine capabilities and limitations.', 'chapters': [{'end': 7569.519, 'start': 7467.93, 'title': 'Defining intelligence: performance and generality', 'summary': "Discusses a functional definition of intelligence as the combination of how much one can do and how well one can do it, allowing for a scientific discussion about machine-level intelligence and enabling the comparison of the relative intelligence of two separate agents. it introduces the equation 'intelligence equals performance times generality.'", 'duration': 101.589, 'highlights': ['The working definition of intelligence proposed is a combination of how much one can do and how well one can do it, suggesting that intelligence is the combination of being able to do many things and to do them well.', 'The functional definition of intelligence allows for a scientific discussion about machine-level intelligence, forming hypotheses that could potentially be falsified, and comparing the relative intelligence of two separate agents.', "Using fake math, the equation 'intelligence equals performance times generality' is introduced, indicating a conceptual framework for understanding intelligence.", 'Notionally, being extremely good at only one thing or doing many things poorly may not be indicative of intelligence, emphasizing the importance of both the quantity and quality of tasks performed.', 'The presentation aims to start a discussion and foster ideas about intelligence, highlighting the practical and useful nature of the proposed definition.', 'There are many potential definitions of intelligence, some of which can be measured experimentally, while others cannot, suggesting the complexity of defining intelligence.']}, {'end': 8187.068, 'start': 7570.339, 'title': 'Human vs machine intelligence', 'summary': 'Discusses the comparison of human and machine intelligence, outlining the diverse aspects of human performance and generality, and examining specific examples such as chess and go-playing programs exceeding human capabilities in certain tasks, while lacking in generality.', 'duration': 616.729, 'highlights': ["Chess program Stockfish has an ELO rating of 3447, surpassing top-rated human player Magnus Carlsen's 2882, showcasing machine superiority in chess.", "AlphaGo Master achieved an ELO rating of 4858, surpassing the highest-rated human player's 66.69, demonstrating the program's dominance in the game of Go.", 'Machines excel in specific tasks such as chess and Go, surpassing human capabilities in these areas, yet lack generality, as demonstrated by the limited usefulness of the Stockfish program beyond chess.']}], 'duration': 719.138, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM7467930.jpg', 'highlights': ["The equation 'intelligence equals performance times generality' introduces a conceptual framework for understanding intelligence.", 'The working definition of intelligence proposed is a combination of how much one can do and how well one can do it, suggesting that intelligence is the combination of being able to do many things and to do them well.', "Using fake math, the equation 'intelligence equals performance times generality' is introduced, indicating a conceptual framework for understanding intelligence.", "Chess program Stockfish has an ELO rating of 3447, surpassing top-rated human player Magnus Carlsen's 2882, showcasing machine superiority in chess.", "AlphaGo Master achieved an ELO rating of 4858, surpassing the highest-rated human player's 66.69, demonstrating the program's dominance in the game of Go."]}, {'end': 9122.683, 'segs': [{'end': 8241.442, 'src': 'embed', 'start': 8188.211, 'weight': 3, 'content': [{'end': 8192.472, 'text': "Because there are so many possible board configurations, however, it can't memorize them all.", 'start': 8188.211, 'duration': 4.261}, {'end': 8198.214, 'text': 'So it uses convolutional neural networks to learn common configurations.', 'start': 8192.952, 'duration': 5.262}, {'end': 8206.137, 'text': "And most importantly, it can see patterns that are not exactly repeated but are similar to things that it's seen before.", 'start': 8199.055, 'duration': 7.082}, {'end': 8208.397, 'text': 'This is an important innovation.', 'start': 8206.797, 'duration': 1.6}, {'end': 8211.799, 'text': "And we'll come back to convolutional neural networks in a few minutes.", 'start': 8209.038, 'duration': 2.761}, {'end': 8221.554, 'text': 'It also uses reinforcement learning on a library of human games to learn which moves are good.', 'start': 8215.146, 'duration': 6.408}, {'end': 8234.789, 'text': 'Reinforcement learning in very straightforward terms is looking at a configuration, an action, and the outcome and learning the pattern.', 'start': 8222.775, 'duration': 12.014}, {'end': 8239.062, 'text': 'For a given configuration, if I take action A, good things happen.', 'start': 8235.68, 'duration': 3.382}, {'end': 8241.442, 'text': 'If I take action B, bad things tend to happen.', 'start': 8239.102, 'duration': 2.34}], 'summary': 'Ai uses convolutional neural networks to learn board configurations and reinforcement learning to understand good moves.', 'duration': 53.231, 'max_score': 8188.211, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM8188211.jpg'}, {'end': 8400.742, 'src': 'embed', 'start': 8376.178, 'weight': 0, 'content': [{'end': 8384.904, 'text': 'Finally, in 2015, the error rate got lower than human, so we had a computer program classifying images better than a human does.', 'start': 8376.178, 'duration': 8.726}, {'end': 8392.589, 'text': 'Now, in 2017, one of the more recent competitions, more than half of the teams got fewer than 5% wrong.', 'start': 8386.905, 'duration': 5.684}, {'end': 8398.22, 'text': 'So now machines are routinely beating humans at this task.', 'start': 8394.437, 'duration': 3.783}, {'end': 8400.742, 'text': 'Pretty impressive.', 'start': 8399.841, 'duration': 0.901}], 'summary': 'In 2017, over half the teams got less than 5% wrong in image classification, surpassing human performance.', 'duration': 24.564, 'max_score': 8376.178, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM8376178.jpg'}, {'end': 8613.987, 'src': 'embed', 'start': 8582.009, 'weight': 2, 'content': [{'end': 8592.018, 'text': 'So definitely higher than human performance, but classifying ImageNet is a much narrower task than it might appear on the surface.', 'start': 8582.009, 'duration': 10.009}, {'end': 8595.721, 'text': "So we'll put it pretty low on the generality axis.", 'start': 8593.038, 'duration': 2.683}, {'end': 8602.584, 'text': "Now here's a really fun example, video game performance.", 'start': 8599.403, 'duration': 3.181}, {'end': 8613.987, 'text': 'So again, the folks at DeepMind put together a deep Q learning or deep reinforcement learning architecture to play video games.', 'start': 8603.284, 'duration': 10.703}], 'summary': 'Deepmind achieves high performance on imagenet classification and video game playing using deep learning.', 'duration': 31.978, 'max_score': 8582.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM8582009.jpg'}], 'start': 8188.211, 'title': 'Advancements in ai applications', 'summary': "Discusses the use of ai in various applications such as alphago's learning process, advancements in image and video recognition including progress in error rates, limitations of ai in video games, language translation, and recommenders, and the achievements and challenges faced in these areas.", 'chapters': [{'end': 8268.374, 'start': 8188.211, 'title': 'Alphago: learning and innovation', 'summary': 'Discusses how alphago uses convolutional neural networks to learn common board configurations, and reinforcement learning on a library of human games to determine good moves, ultimately leading to its success in go.', 'duration': 80.163, 'highlights': ['AlphaGo uses convolutional neural networks to learn common board configurations, enabling it to see patterns similar to those it has encountered before.', 'AlphaGo employs reinforcement learning on a library of human games to determine good moves by analyzing the outcome of actions, allowing it to make better decisions in subsequent games.', 'AlphaGo initially learns from the history of human play and insights using reinforcement learning, bootstrapping its success in Go.', "AlphaGo's approach is an important innovation as it allows it to process patterns that are not exact repetitions, contributing to its success in playing Go."]}, {'end': 8724.533, 'start': 8269.074, 'title': 'Ai advancements in image and video recognition', 'summary': 'Discusses the advancements in image and video recognition, highlighting the progress in image classification error rates, convolutional neural network limitations, and deep reinforcement learning in video game performance.', 'duration': 455.459, 'highlights': ['In 2017, more than half of the teams in the image classification competition achieved fewer than 5% error rates, surpassing human performance.', 'The error rate in image classification decreased by close to half each year, with the 2015 error rate being lower than that of humans.', "DeepMind's deep reinforcement learning architecture achieved at or above human expert level play in 29 out of 49 classic Atari games."]}, {'end': 9122.683, 'start': 8724.533, 'title': 'Ai in video games, language translation, and recommenders', 'summary': "Discusses ai's performance in video games, language translation, and recommenders, highlighting its challenges and achievements, including the inability to match human performance in certain games, impressive multilingual translation capabilities, and the limitations of recommender systems in capturing changing preferences.", 'duration': 398.15, 'highlights': ['Language translation involves over a hundred languages translated to an intermediate representation and then back into any one of these languages, but the state-of-the-art translation is far from perfect.', "AI's performance in video games is below human level, particularly in games requiring longer-term planning, such as Ms. Pac-Man and Montezuma's Revenge.", 'Recommender systems often provide recommendations with only one in ten being really relevant, and they do not adapt to changing preferences over time.']}], 'duration': 934.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM8188211.jpg', 'highlights': ['In 2017, more than half of the teams in the image classification competition achieved fewer than 5% error rates, surpassing human performance.', 'The error rate in image classification decreased by close to half each year, with the 2015 error rate being lower than that of humans.', "DeepMind's deep reinforcement learning architecture achieved at or above human expert level play in 29 out of 49 classic Atari games.", 'AlphaGo employs reinforcement learning on a library of human games to determine good moves by analyzing the outcome of actions, allowing it to make better decisions in subsequent games.', 'AlphaGo uses convolutional neural networks to learn common board configurations, enabling it to see patterns similar to those it has encountered before.']}, {'end': 11763.199, 'segs': [{'end': 9188.097, 'src': 'embed', 'start': 9160.806, 'weight': 0, 'content': [{'end': 9169.329, 'text': 'taken overall, per mile driven self-driving cars, accident rates are lower than humans,', 'start': 9160.806, 'duration': 8.523}, {'end': 9181.273, 'text': 'and this is pretty amazing when you consider all of the things that a car has to deal with construction, pedestrians, bicycle riders,', 'start': 9169.329, 'duration': 11.944}, {'end': 9183.874, 'text': 'changing weather conditions, changing road conditions.', 'start': 9181.273, 'duration': 2.601}, {'end': 9188.097, 'text': "They're not perfect, but they're surprisingly good.", 'start': 9184.616, 'duration': 3.481}], 'summary': 'Self-driving cars have lower accident rates than humans per mile driven.', 'duration': 27.291, 'max_score': 9160.806, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM9160806.jpg'}, {'end': 9710.855, 'src': 'embed', 'start': 9675.432, 'weight': 1, 'content': [{'end': 9685.418, 'text': 'And it turns out that when using this approach with Go, within four hours, it was as good as the best human player.', 'start': 9675.432, 'duration': 9.986}, {'end': 9694.384, 'text': 'And within eight hours, it had beat the previous best computer, its uncle, AlphaGo.', 'start': 9686.219, 'duration': 8.165}, {'end': 9700.132, 'text': 'Because it did not build any rules of the game.', 'start': 9696.591, 'duration': 3.541}, {'end': 9710.855, 'text': 'it was also able to learn chess and beat the current best chess playing program, Stockfish and another board game called Shogi,', 'start': 9700.132, 'duration': 10.723}], 'summary': 'Ai mastered go in 8 hours, beat alphago, and learned chess and shogi.', 'duration': 35.423, 'max_score': 9675.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM9675432.jpg'}, {'end': 10166.464, 'src': 'embed', 'start': 10134.116, 'weight': 2, 'content': [{'end': 10149.168, 'text': 'So my proposal is that focusing on physical interaction is a great way to force us to confront these assumptions, to find out which ones we can bend,', 'start': 10134.116, 'duration': 15.052}, {'end': 10151.51, 'text': 'to find out which we can avoid altogether,', 'start': 10149.168, 'duration': 2.342}, {'end': 10161.018, 'text': 'and to drive us to create algorithms that are less fragile and able to accommodate a much more general set of tasks.', 'start': 10151.51, 'duration': 9.508}, {'end': 10166.464, 'text': 'That will then take us one step closer to human level intelligence.', 'start': 10162.062, 'duration': 4.402}], 'summary': 'Focusing on physical interaction can lead to less fragile algorithms and closer to human level intelligence.', 'duration': 32.348, 'max_score': 10134.116, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM10134116.jpg'}, {'end': 10243.658, 'src': 'embed', 'start': 10216.069, 'weight': 3, 'content': [{'end': 10225.212, 'text': 'You can pair this with reinforcement learning algorithms to get algorithms that play video games, learn to play Go, or even control robots.', 'start': 10216.069, 'duration': 9.143}, {'end': 10230.394, 'text': 'So to dig into how these work.', 'start': 10228.133, 'duration': 2.261}, {'end': 10232.314, 'text': "we'll start with a very simple example,", 'start': 10230.394, 'duration': 1.92}, {'end': 10243.658, 'text': "much simpler than all of these a convolutional neural network that can look at a very small image and determine whether it's a picture of an X or an O just two categories.", 'start': 10232.314, 'duration': 11.344}], 'summary': 'Pairing with reinforcement learning can create algorithms for video games, go, and robots. starting with a simple example of a convolutional neural network classifying x and o images.', 'duration': 27.589, 'max_score': 10216.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM10216069.jpg'}, {'end': 10643.762, 'src': 'embed', 'start': 10606.625, 'weight': 4, 'content': [{'end': 10612.847, 'text': 'So this is what a convolution layer in a convolutional neural network does.', 'start': 10606.625, 'duration': 6.222}, {'end': 10618.869, 'text': 'It has a set of features in it and it can be three or 30 or 300 or 3000,', 'start': 10614.348, 'duration': 4.521}, {'end': 10632.937, 'text': 'but it has a set of features and it takes the original image and returns a set of filtered images, one for each of the features.', 'start': 10618.869, 'duration': 14.068}, {'end': 10636.098, 'text': "So this is how we'll represent it.", 'start': 10634.618, 'duration': 1.48}, {'end': 10643.762, 'text': "That's the number one ingredient in convolutional neural networks.", 'start': 10640.02, 'duration': 3.742}], 'summary': 'A convolution layer in a cnn has a set of features and returns filtered images for each feature, a key ingredient in cnns.', 'duration': 37.137, 'max_score': 10606.625, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM10606625.jpg'}, {'end': 10949.275, 'src': 'embed', 'start': 10925.462, 'weight': 5, 'content': [{'end': 10933.469, 'text': "Now, by the time we've gone through several iterations of this, we take and run it through a fully connected layer.", 'start': 10925.462, 'duration': 8.007}, {'end': 10945.013, 'text': 'This is more of a standard neural network where every input gets connected to everything in the next layer with a weight.', 'start': 10934.089, 'duration': 10.924}, {'end': 10949.275, 'text': 'Every single value, you can think of it as a voting process.', 'start': 10946.634, 'duration': 2.641}], 'summary': 'Iterative process leads to a fully connected neural network with voting process.', 'duration': 23.813, 'max_score': 10925.462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM10925462.jpg'}, {'end': 11189.871, 'src': 'embed', 'start': 11161.752, 'weight': 6, 'content': [{'end': 11169.557, 'text': 'The numbers correspond to the brightness, minus one is black, plus one is white, zero is middle gray, and everything else is in between.', 'start': 11161.752, 'duration': 7.805}, {'end': 11174.981, 'text': 'So this takes this little image and turns it into a list of numbers.', 'start': 11171.539, 'duration': 3.442}, {'end': 11176.962, 'text': "That's our input vector.", 'start': 11175.761, 'duration': 1.201}, {'end': 11181.765, 'text': 'Now, each of these, you can think of it as having a receptive field.', 'start': 11178.663, 'duration': 3.102}, {'end': 11189.871, 'text': 'This is an image which makes the value of this input as high as possible.', 'start': 11182.666, 'duration': 7.205}], 'summary': 'Image processing transforms image into list of numbers with bright to dark scale.', 'duration': 28.119, 'max_score': 11161.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM11161752.jpg'}, {'end': 11267.009, 'src': 'embed', 'start': 11236.479, 'weight': 7, 'content': [{'end': 11238.7, 'text': 'We mentioned the weighted voting process before.', 'start': 11236.479, 'duration': 2.221}, {'end': 11248, 'text': 'So what that looks like is each of these inputs gets assigned a weight between plus and minus 1.', 'start': 11239.18, 'duration': 8.82}, {'end': 11251.602, 'text': 'the value gets multiplied by that weight before it gets added.', 'start': 11248, 'duration': 3.602}, {'end': 11255.824, 'text': 'So now we have a weighted sum of these input neurons.', 'start': 11252.342, 'duration': 3.482}, {'end': 11261.306, 'text': 'And we will represent this visually by showing positive weights in white,', 'start': 11257.404, 'duration': 3.902}, {'end': 11267.009, 'text': 'negative weights in black and the thickness of the line being approximately proportional to the weight.', 'start': 11261.306, 'duration': 5.703}], 'summary': 'Weighted voting assigns inputs a weight between plus and minus 1, multiplied before added.', 'duration': 30.53, 'max_score': 11236.479, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM11236479.jpg'}], 'start': 9126.268, 'title': 'Ai performance, generality, and limitations', 'summary': "Discusses the performance and generality of self-driving cars and humanoid robots, noting self-driving cars' lower accident rates than humans and the limitations of humanoid robots. it also addresses common assumptions in ai algorithms, the role of physical interaction in confronting these assumptions, and the understanding of convolutional neural networks and neural network layers in categorizing and processing images.", 'chapters': [{'end': 9929.679, 'start': 9126.268, 'title': 'Performance and generality of self-driving cars and humanoid robots', 'summary': 'Discusses the performance and generality of self-driving cars and humanoid robots, highlighting that self-driving cars demonstrate lower accident rates than humans per mile driven, while their generality is constrained by the need to reduce the difficulty of the task. on the other hand, humanoid robots struggle with simple tasks, making assumptions about hardware and environment, and their performance is sometimes laughably low compared to humans. the chapter also presents the case of alphazero, a program that achieved both higher performance and generality without specific rules, beating human players in board games and showcasing a real increase in intelligence.', 'duration': 803.411, 'highlights': ['Self-driving cars demonstrate lower accident rates than humans per mile driven, considering various factors such as construction, pedestrians, changing weather and road conditions, but their generality is constrained by the need to reduce the difficulty of the task.', 'Humanoid robots struggle with simple tasks like opening a door or lifting an empty box due to complex systems, assumptions about hardware and environment, and their performance is sometimes laughably low compared to humans.', 'AlphaZero, a program that achieved higher performance and generality without specific rules, beat human players in board games within four hours and showcased a real increase in intelligence, emphasizing the importance of making fewer assumptions and practicing as many times as needed through self-play.']}, {'end': 10604.244, 'start': 9930.259, 'title': 'Limitations of ai assumptions', 'summary': 'Discusses common assumptions in ai algorithms, their limitations, and the proposal to focus on physical interaction as a way to confront and overcome these assumptions, leading to advancements in human-level intelligence. it also provides an overview of convolutional neural networks and their application in pattern recognition and matching.', 'duration': 673.985, 'highlights': ['The proposal to focus on physical interaction as a means to confront and overcome common assumptions in AI algorithms, leading to advancements in human-level intelligence.', 'Explanation of convolutional neural networks and their ability to learn patterns in images, such as line segments and complex objects, and their application in tasks like playing video games, Go, and controlling robots.', 'Detailed explanation of how convolutional neural networks use filtering to match features across image patches, providing a strong understanding of the matching process.']}, {'end': 11081.716, 'start': 10606.625, 'title': 'Convolutional neural networks: understanding layers', 'summary': 'Explains the key components of a convolutional neural network, including the function of convolution layers in creating filtered images, the process of max pooling to shrink down images, and the role of normalization in maintaining numerical stability, while emphasizing the iterative nature of these processes and their impact on the final output.', 'duration': 475.091, 'highlights': ["A convolution layer creates a set of filtered images from the original image, with the number of features determining the quantity of filtered images, impacting the network's ability to identify matches.", 'Max pooling involves shrinking the filtered images by picking the maximum value within a window, resulting in a smaller image that retains the original signal and high points, contributing to the reduction in size while maintaining similarity to the original.', "Normalization, through rectified linear unit function, ensures that the network's mathematical operations remain stable by converting negative values to zero, thereby transforming a stack of images into a stack of images with no negative values.", 'The iterative nature of these processes, including convolution, pooling, and normalization, leads to a tall stack of filtered images with no negative values, contributing to the cumulative impact on the final output.', 'The fully connected layer acts as a standard neural network where every input connects to everything in the next layer with a weight, facilitating a weighted voting process to categorize the input, showcasing the final stage of processing in the network.']}, {'end': 11352.546, 'start': 11082.827, 'title': 'Neural networks: categorizing images', 'summary': 'Explains the process of categorizing images with a two by two pixel camera, converting pixel values into input neurons, adding weights to inputs, and using a sigmoid squashing function to ensure the output is between plus and minus 1.', 'duration': 269.719, 'highlights': ['The process of converting pixel values into input neurons involves turning the pixels into a list of numbers, where minus one is black, plus one is white, and zero is middle gray.', 'The weighted sum of the inputs is obtained by assigning each input a weight between plus and minus 1 and multiplying the value by that weight before adding them up.', 'The usage of a sigmoid squashing function, specifically the hyperbolic tangent, ensures that the output is always between plus and minus 1, preventing numerical growth.']}, {'end': 11763.199, 'start': 11355.327, 'title': 'Neural network layers and receptive fields', 'summary': 'Explains the process of applying a function to the output of weighted sum and squash neurons, creating layers of neurons with different receptive fields, and using rectified linear unit neurons to discern patterns, leading to the final output layer with four categories.', 'duration': 407.872, 'highlights': ['The process of applying a function to the output of weighted sum and squash neurons', 'Creating layers of neurons with different receptive fields', 'Using rectified linear unit neurons to discern patterns', 'Leading to the final output layer with four categories']}], 'duration': 2636.931, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM9126268.jpg', 'highlights': ['Self-driving cars demonstrate lower accident rates than humans per mile driven, considering various factors such as construction, pedestrians, changing weather and road conditions.', 'AlphaZero, a program that achieved higher performance and generality without specific rules, beat human players in board games within four hours and showcased a real increase in intelligence.', 'The proposal to focus on physical interaction as a means to confront and overcome common assumptions in AI algorithms, leading to advancements in human-level intelligence.', 'Explanation of convolutional neural networks and their ability to learn patterns in images, such as line segments and complex objects, and their application in tasks like playing video games, Go, and controlling robots.', "A convolution layer creates a set of filtered images from the original image, with the number of features determining the quantity of filtered images, impacting the network's ability to identify matches.", 'The fully connected layer acts as a standard neural network where every input connects to everything in the next layer with a weight, facilitating a weighted voting process to categorize the input, showcasing the final stage of processing in the network.', 'The process of converting pixel values into input neurons involves turning the pixels into a list of numbers, where minus one is black, plus one is white, and zero is middle gray.', 'The weighted sum of the inputs is obtained by assigning each input a weight between plus and minus 1 and multiplying the value by that weight before adding them up.']}, {'end': 13848.44, 'segs': [{'end': 12030.792, 'src': 'embed', 'start': 12004.923, 'weight': 3, 'content': [{'end': 12010.444, 'text': 'So gradient descent is the first level trick.', 'start': 12004.923, 'duration': 5.521}, {'end': 12013.642, 'text': 'for brewing fewer cups of tea.', 'start': 12011.601, 'duration': 2.041}, {'end': 12016.744, 'text': "There's another thing you can do, which is to use curvature.", 'start': 12014.082, 'duration': 2.662}, {'end': 12025.909, 'text': 'This is kind of an advanced method, is you can make your original cup of tea and then make one a little bit warmer and one a little bit cooler.', 'start': 12016.904, 'duration': 9.005}, {'end': 12030.792, 'text': 'And you can look to see how that curve of your function goes.', 'start': 12026.309, 'duration': 4.483}], 'summary': 'Use gradient descent for brewing fewer cups of tea, consider using curvature for advanced method.', 'duration': 25.869, 'max_score': 12004.923, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM12004923.jpg'}, {'end': 12555.34, 'src': 'embed', 'start': 12530.723, 'weight': 4, 'content': [{'end': 12537.409, 'text': 'And we can say, what is the value of the guess That gives me the lowest loss.', 'start': 12530.723, 'duration': 6.686}, {'end': 12540.951, 'text': "That's what that notation means right there.", 'start': 12537.489, 'duration': 3.462}, {'end': 12545.554, 'text': 'And this best guess is just about 55 and a half M&Ms.', 'start': 12541.772, 'duration': 3.782}, {'end': 12547.535, 'text': 'Problem solved.', 'start': 12546.735, 'duration': 0.8}, {'end': 12555.34, 'text': 'So this is an example of numerical optimization where we calculate this loss function.', 'start': 12548.156, 'duration': 7.184}], 'summary': 'Numerical optimization finds best guess of 55.5 m&ms, minimizing loss function.', 'duration': 24.617, 'max_score': 12530.723, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM12530723.jpg'}, {'end': 12812.629, 'src': 'embed', 'start': 12775.316, 'weight': 2, 'content': [{'end': 12776.016, 'text': "Here's our error.", 'start': 12775.316, 'duration': 0.7}, {'end': 12784.69, 'text': "Now, the idea with gradient descent is we're not just adjusting one thing.", 'start': 12779.585, 'duration': 5.105}, {'end': 12787.292, 'text': "We're not just adjusting our guess of the number of M&Ms.", 'start': 12784.71, 'duration': 2.582}, {'end': 12790.275, 'text': "We're adjusting many things.", 'start': 12788.113, 'duration': 2.162}, {'end': 12798.483, 'text': 'We want to go through and adjust every single weight in every single layer to bring this error down a little bit.', 'start': 12790.595, 'duration': 7.888}, {'end': 12812.629, 'text': 'Now that is a little bit challenging to do, because in order to do that, one thing you can do is find a analytical solution, like we did before.', 'start': 12800.814, 'duration': 11.815}], 'summary': 'Gradient descent adjusts every weight to minimize error in neural networks', 'duration': 37.313, 'max_score': 12775.316, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM12775316.jpg'}, {'end': 13366.125, 'src': 'embed', 'start': 13340.246, 'weight': 1, 'content': [{'end': 13347.949, 'text': 'In none of these steps have we had to recalculate all of the values in the neural network.', 'start': 13340.246, 'duration': 7.703}, {'end': 13354.851, 'text': "We've been able to rely on things that have already been calculated, what the values are at each of these neurons.", 'start': 13348.669, 'duration': 6.182}, {'end': 13366.125, 'text': "That's what makes backpropagation so mathematically efficient and is what allows us to efficiently train neural networks.", 'start': 13355.712, 'duration': 10.413}], 'summary': 'Backpropagation efficiently utilizes precalculated values to train neural networks.', 'duration': 25.879, 'max_score': 13340.246, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM13340246.jpg'}, {'end': 13521.788, 'src': 'embed', 'start': 13492.874, 'weight': 0, 'content': [{'end': 13498.738, 'text': 'Give it a bunch of inputs that we know the right answer to and we let it adjust all of those connections.', 'start': 13492.874, 'duration': 5.864}, {'end': 13508.563, 'text': 'Not only that, it also adjusts all of the pixels in the features for each convolutional layer.', 'start': 13499.799, 'duration': 8.764}, {'end': 13512.144, 'text': 'So it learns not only the weights, but also the features.', 'start': 13508.603, 'duration': 3.541}, {'end': 13521.788, 'text': 'And then over time, those representations become something that lets it predict very well what is an X and what is an O.', 'start': 13513.344, 'duration': 8.444}], 'summary': 'Ai adjusts connections and features to predict x and o accurately.', 'duration': 28.914, 'max_score': 13492.874, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM13492874.jpg'}, {'end': 13844.937, 'src': 'heatmap', 'start': 13713.856, 'weight': 1, 'content': [{'end': 13723.48, 'text': 'You can make each of your rows a different word in the dictionary and then you can make your columns the position in sentence or a position location that it occurs in time.', 'start': 13713.856, 'duration': 9.624}, {'end': 13728.529, 'text': 'Now, there are some limitations here.', 'start': 13726.769, 'duration': 1.76}, {'end': 13735.271, 'text': 'Convolutional neural networks only capture local spatial patterns.', 'start': 13730.25, 'duration': 5.021}, {'end': 13741.493, 'text': "So if your data can't be made to look like an image, or if it doesn't make sense to, then they're less useful.", 'start': 13735.851, 'duration': 5.642}, {'end': 13751.755, 'text': 'So, for example, imagine you have customer data that has columns representing things like names and ages, addresses, emails, purchase transactions,', 'start': 13742.613, 'duration': 9.142}, {'end': 13754.356, 'text': 'browsing histories, and these customers are listed.', 'start': 13751.755, 'duration': 2.601}, {'end': 13764.41, 'text': "If you were to rearrange the rows or rearrange the columns, the information itself wouldn't really be compromised.", 'start': 13755.683, 'duration': 8.727}, {'end': 13765.411, 'text': 'It would all still be there.', 'start': 13764.511, 'duration': 0.9}, {'end': 13767.893, 'text': 'It would also be queryable, searchable, and interpretable.', 'start': 13765.431, 'duration': 2.462}, {'end': 13771.416, 'text': "Convolutional neural networks don't help you here.", 'start': 13769.535, 'duration': 1.881}, {'end': 13773.678, 'text': 'They look for spatial patterns.', 'start': 13771.877, 'duration': 1.801}, {'end': 13780.084, 'text': 'So if the spatial organization of your data is not important, it will not be able to find what matters.', 'start': 13774.139, 'duration': 5.945}, {'end': 13791.775, 'text': "So rule of thumb, if your data is just as useful after swapping your columns with each other, then you can't use convolutional neural networks.", 'start': 13782.267, 'duration': 9.508}, {'end': 13793.556, 'text': "You shouldn't use convolutional neural networks.", 'start': 13791.835, 'duration': 1.721}, {'end': 13795.338, 'text': "That's a big takeaway from this.", 'start': 13794.217, 'duration': 1.121}, {'end': 13801.903, 'text': "So they're really good at finding patterns and using them to classify images.", 'start': 13797.9, 'duration': 4.003}, {'end': 13803.625, 'text': 'That is what they are the best at.', 'start': 13801.943, 'duration': 1.682}, {'end': 13811.629, 'text': 'Now, the takeaway from this is not that you should go and code up your own convolutional neural networks from scratch.', 'start': 13805.322, 'duration': 6.307}, {'end': 13813.511, 'text': 'You can.', 'start': 13813.071, 'duration': 0.44}, {'end': 13814.572, 'text': "It's a great exercise.", 'start': 13813.571, 'duration': 1.001}, {'end': 13815.333, 'text': "It's a lot of fun.", 'start': 13814.612, 'duration': 0.721}, {'end': 13823.022, 'text': 'But when you go to actually use it, there are a lot of mature tools out there that are helpful and just waiting to be applied to this.', 'start': 13815.734, 'duration': 7.288}, {'end': 13832.928, 'text': 'The takeaway from this is that you will be asked to make a lot of subtle decisions about how to prepare your data and feed it in,', 'start': 13824.261, 'duration': 8.667}, {'end': 13836.631, 'text': 'how to interpret the results and how to choose these hyper parameters for this.', 'start': 13832.928, 'duration': 3.703}, {'end': 13844.937, 'text': "It helps to know what's going to be done with your data and what it all means so that you can get the most out of these tools.", 'start': 13837.531, 'duration': 7.406}], 'summary': 'Convolutional neural networks only capture local spatial patterns and are best for classifying images.', 'duration': 131.081, 'max_score': 13713.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM13713856.jpg'}], 'start': 11763.659, 'title': 'Optimization in machine learning', 'summary': 'Covers optimization in machine learning, with topics including gradient descent, advanced techniques, addressing challenges like noisy data and discrete jumps in functions. it also explores numerical optimization and calculus to achieve the best guess for the number of m&ms in a bag, and discusses the use of optimization and gradient descent in neural networks, back propagation principles, and the efficiency of backpropagation in training convolutional neural networks.', 'chapters': [{'end': 12294.705, 'start': 11763.659, 'title': 'Optimizing with gradient descent', 'summary': 'Discusses optimization in machine learning, introducing gradient descent as a method to minimize suffering and find the best performance, followed by advanced techniques like using curvature and addressing challenges like noisy data and discrete jumps in functions.', 'duration': 531.046, 'highlights': ['Gradient descent is introduced as a method to minimize suffering and find the best performance in optimization problems.', 'The concept of using curvature as an advanced optimization technique is explained, enabling more efficient steps towards finding the bottom of the curve.', 'Challenges in optimization, such as dealing with noisy data and discrete jumps in functions, are addressed as potential obstacles in the optimization process.', 'Advanced methods like genetic algorithms and simulated annealing are mentioned as alternatives to gradient descent, offering more robustness and sample efficiency.', 'The analogy of optimization methods to different vehicles, such as Formula One race cars, four-wheel drive pickup trucks, and traveling on foot, is used to illustrate their characteristics and trade-offs.']}, {'end': 12687.985, 'start': 12295.266, 'title': 'Optimizing m&m bag guess', 'summary': 'Explores the process of optimizing the guess for the number of m&ms in a bag by minimizing the deviation using a loss function, leading to a best guess of around 55.5 m&ms, achieved through numerical optimization and calculus.', 'duration': 392.719, 'highlights': ['The best guess for the number of M&Ms in a bag is around 55.5, achieved by minimizing the deviation using a loss function through numerical optimization and calculus.', 'The deviation is turned into a cost by squaring it, penalizing the further off guesses more, and this is expressed through a loss function.', 'The theoretical best guess for the number of M&Ms in a bag is the sum total of the number of M&Ms found in all the bags divided by the number of bags, or the average count per bag.']}, {'end': 12971.625, 'start': 12688.005, 'title': 'Neural network optimization', 'summary': 'Discusses the use of optimization and gradient descent in neural networks, highlighting the process of adjusting weights to minimize error and the use of back propagation to efficiently calculate the slope for weight adjustment.', 'duration': 283.62, 'highlights': ['The process of gradient descent involves adjusting every single weight in every single layer to minimize error, which can be challenging due to the large number of weights, possibly in the hundreds or millions.', 'Back propagation allows for the efficient calculation of the slope without the need to recalculate the value of everything each time, enabling the adjustment of weights based on the change in error.', 'Analytically computing the slope of the function in neural networks allows for the determination of the direction and extent of weight adjustment, contributing to the optimization process.']}, {'end': 13339.505, 'start': 12972.886, 'title': 'Neural network back propagation', 'summary': 'Discusses the principles of back propagation in a neural network, explaining how changes in input weights affect the error, and how the chaining of these changes helps in calculating the slope of the loss function, with practical examples and derivations.', 'duration': 366.619, 'highlights': ['In a simple neural network, the derivative of the output with respect to a weight is the input value, facilitating the understanding of how changes in weights affect the output.', 'Back propagation involves chaining the effects of changing weights to calculate the slope of the loss function, enabling the determination of how a change in a weight affects the error.', 'The process of back propagation involves breaking down the impact of changing weights on the error into sequential steps, allowing for the calculation of the slope of the loss function with respect to specific weights.', 'The back propagation process can be applied iteratively to determine the impact of changing weights deep within the neural network, enabling the calculation of the slope of the loss function with respect to these weights.', "The sigmoid function's derivative simplifies to the value of the function times 1 minus the value of the function, making it straightforward to calculate in the back propagation process."]}, {'end': 13848.44, 'start': 13340.246, 'title': 'Backpropagation and convolutional neural networks', 'summary': 'Explains the mathematical efficiency of backpropagation in training neural networks, the process of adjusting weights and features to improve prediction accuracy, and the suitability of convolutional neural networks for data with local spatial patterns, with emphasis on the importance of hyperparameters and data preparation.', 'duration': 508.194, 'highlights': ['Backpropagation is mathematically efficient, allowing for efficient training of neural networks by adjusting weights based on labeled examples, resulting in improved prediction accuracy after iterations.', 'Convolutional neural networks are trained by adjusting both weights and features based on labeled examples, resulting in representations that enable accurate prediction of patterns in the data.', 'Hyperparameters, such as the number of features in convolutional layers and the size of pooling windows, play a crucial role in controlling the performance of neural networks and can be optimized to achieve the best results.', 'Convolutional neural networks are suitable for data with local spatial patterns, such as images and sound, where the order and proximity of elements convey important information, but may be less useful for non-spatial data.']}], 'duration': 2084.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/dPWYUELwIdM/pics/dPWYUELwIdM11763659.jpg', 'highlights': ['Convolutional neural networks are trained by adjusting both weights and features based on labeled examples, resulting in representations that enable accurate prediction of patterns in the data.', 'Backpropagation is mathematically efficient, allowing for efficient training of neural networks by adjusting weights based on labeled examples, resulting in improved prediction accuracy after iterations.', 'The process of gradient descent involves adjusting every single weight in every single layer to minimize error, which can be challenging due to the large number of weights, possibly in the hundreds or millions.', 'The concept of using curvature as an advanced optimization technique is explained, enabling more efficient steps towards finding the bottom of the curve.', 'The best guess for the number of M&Ms in a bag is around 55.5, achieved by minimizing the deviation using a loss function through numerical optimization and calculus.']}], 'highlights': ["AlphaGo Master achieved an ELO rating of 4858, surpassing the highest-rated human player's 66.69, demonstrating the program's dominance in the game of Go.", 'AlphaGo employs reinforcement learning on a library of human games to determine good moves by analyzing the outcome of actions, allowing it to make better decisions in subsequent games.', 'AlphaZero, a program that achieved higher performance and generality without specific rules, beat human players in board games within four hours and showcased a real increase in intelligence.', 'Self-driving cars demonstrate lower accident rates than humans per mile driven, considering various factors such as construction, pedestrians, changing weather and road conditions.', 'In 2017, more than half of the teams in the image classification competition achieved fewer than 5% error rates, surpassing human performance.']}