title
MIT 6.S191 (2020): Convolutional Neural Networks

description
MIT Introduction to Deep Learning 6.S191: Lecture 3 Convolutional Neural Networks for Computer Vision Lecturer: Alexander Amini January 2020 For all lectures, slides, and lab materials: http://introtodeeplearning.com Lecture Outline 0:00 - Introduction 3:04 - What computers "see" 8:06 - Learning visual features 12:36 - Feature extraction and convolution 19:12 - Convolution neural networks 24:03 - Non-linearity and pooling 28:30 - Code example 29:32 - Applications 32:53 - End-to-end self driving cars 35:55 - Summary Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

detail
{'title': 'MIT 6.S191 (2020): Convolutional Neural Networks', 'heatmap': [{'end': 1233.407, 'start': 1209.021, 'weight': 1}], 'summary': 'Explores the impact of deep learning on computer vision, including facial recognition and applications in healthcare, medicine, and autonomous vehicles. it delves into the fundamentals of computer vision, image classification, learning visual features and spatial structure in neural networks, convolutional neural networks, cnn basics, and cnn in image analysis. it also covers the application of neural networks in healthcare, self-driving cars, and computer vision.', 'chapters': [{'end': 188.557, 'segs': [{'end': 103.616, 'src': 'embed', 'start': 51.311, 'weight': 0, 'content': [{'end': 65.546, 'text': "Today we're going to be learning about how deep learning can build powerful computer vision systems capable of solving extraordinary complex tasks that maybe just 15 years ago would have not even been possible to be solved.", 'start': 51.311, 'duration': 14.235}, {'end': 77.413, 'text': 'Now, one example of how deep learning is transforming computer vision is facial recognition.', 'start': 66.283, 'duration': 11.13}, {'end': 80.315, 'text': 'So, on the top left, you can see an icon of the human eye,', 'start': 77.593, 'duration': 2.722}, {'end': 87.041, 'text': 'which visually represents vision coming into a deep neural network in the form of images or pixels or video.', 'start': 80.315, 'duration': 6.726}, {'end': 93.431, 'text': 'And on the output on the bottom you can see a depiction of a human face or detecting human face,', 'start': 87.862, 'duration': 5.569}, {'end': 100.223, 'text': 'but this could also be recognizing different human faces or even emotions on the face, recognizing key facial features, et cetera.', 'start': 93.431, 'duration': 6.792}, {'end': 103.616, 'text': 'Now, deep learning has transformed this field,', 'start': 101.414, 'duration': 2.202}], 'summary': 'Deep learning enables powerful computer vision systems, transforming tasks previously deemed impossible, such as facial recognition and emotion detection.', 'duration': 52.305, 'max_score': 51.311, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI51311.jpg'}, {'end': 182.951, 'src': 'embed', 'start': 149.813, 'weight': 2, 'content': [{'end': 153.434, 'text': 'Now. another common example is in the context of self-driving cars,', 'start': 149.813, 'duration': 3.621}, {'end': 157.975, 'text': 'where we take an image as input and try to learn an autonomous control system for that car.', 'start': 153.434, 'duration': 4.541}, {'end': 160.095, 'text': 'This is all entirely end-to-end.', 'start': 158.435, 'duration': 1.66}, {'end': 165.476, 'text': 'So we have vision and pixels coming in as input and the actuation of the car coming in as output.', 'start': 160.135, 'duration': 5.341}, {'end': 171.277, 'text': 'Now, this is radically different than the vast majority of autonomous car companies and how they operate.', 'start': 166.696, 'duration': 4.581}, {'end': 175.818, 'text': 'So if you look at companies like Waymo and Tesla, this end-to-end approach is radically different.', 'start': 171.297, 'duration': 4.521}, {'end': 182.951, 'text': "We'll talk more about this later on, but this is actually just one of the autonomous vehicles that we built here as part of my lab at CSAIL,", 'start': 177.005, 'duration': 5.946}], 'summary': 'End-to-end approach for autonomous car control is different from most companies like waymo and tesla.', 'duration': 33.138, 'max_score': 149.813, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI149813.jpg'}], 'start': 9.947, 'title': 'Deep learning in computer vision', 'summary': 'Explores the impact of deep learning on computer vision, emphasizing its role in facial recognition and its applications in healthcare, medicine, and autonomous vehicles, highlighting the transformation of complex tasks and the potential for end-to-end systems.', 'chapters': [{'end': 188.557, 'start': 9.947, 'title': 'Deep learning in computer vision', 'summary': "Explores deep learning's impact on computer vision, highlighting its role in facial recognition and its application in healthcare, medicine, and autonomous vehicles, emphasizing the transformation of complex tasks and the potential for end-to-end systems.", 'duration': 178.61, 'highlights': ['Deep learning has transformed computer vision, enabling complex tasks like facial recognition and disease region detection, with the ability to swap out tasks by providing vast amounts of data for the algorithm to learn, revolutionizing healthcare, medicine, and autonomous vehicles.', 'The significance of vision in human life is emphasized, with a focus on navigation, manipulation, object recognition, and the recognition of complex human emotion and behaviors.', 'The chapter discusses the application of deep learning in autonomous vehicles, highlighting an end-to-end approach for learning autonomous control systems from input images and the actuation of the car as output, contrasting it with the traditional approaches of companies like Waymo and Tesla.']}], 'duration': 178.61, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI9947.jpg', 'highlights': ['Deep learning revolutionizes healthcare, medicine, and autonomous vehicles with complex task capabilities and vast data (relevance: 5)', "Deep learning emphasizes vision's significance in human life, including navigation, object recognition, and emotion detection (relevance: 4)", 'Deep learning applies an end-to-end approach for learning autonomous control systems from input images, contrasting traditional approaches (relevance: 3)']}, {'end': 433.146, 'segs': [{'end': 214.855, 'src': 'embed', 'start': 188.557, 'weight': 1, 'content': [{'end': 194.903, 'text': 'some of the computer vision tasks that we as humans solve every single day and that we can also train machines to solve,', 'start': 188.557, 'duration': 6.346}, {'end': 199.168, 'text': 'the next natural question I think to ask is how can computers see?', 'start': 194.903, 'duration': 4.265}, {'end': 204.067, 'text': 'And specifically, How does a computer process an image or a video??', 'start': 199.408, 'duration': 4.659}, {'end': 207.53, 'text': 'How do they process pixels coming from those images?', 'start': 204.547, 'duration': 2.983}, {'end': 211.232, 'text': 'Well, to a computer, images are just numbers.', 'start': 209.331, 'duration': 1.901}, {'end': 214.855, 'text': 'And suppose we have this picture here of Abraham Lincoln.', 'start': 211.933, 'duration': 2.922}], 'summary': 'Computers process images as numbers to solve computer vision tasks.', 'duration': 26.298, 'max_score': 188.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI188557.jpg'}, {'end': 277.127, 'src': 'embed', 'start': 231.753, 'weight': 0, 'content': [{'end': 237.397, 'text': 'It sees it as just a matrix of two-dimensional numbers, or two-dimensional matrix of numbers, rather.', 'start': 231.753, 'duration': 5.644}, {'end': 242.112, 'text': 'Now, if we have an RGB image, a color image instead of a grayscale image,', 'start': 238.629, 'duration': 3.483}, {'end': 249.919, 'text': 'we can simply represent that as three of these two-dimensional images concatenated or stacked on top of each other one for the red channel,', 'start': 242.112, 'duration': 7.807}, {'end': 252.981, 'text': "one for the green channel, one for the blue channel, and that's RGB.", 'start': 249.919, 'duration': 3.062}, {'end': 263.651, 'text': 'Now we have a way to represent images to computers and we can think about what types of computer vision tasks this will allow us to solve and what we can perform given this foundation.', 'start': 254.042, 'duration': 9.609}, {'end': 273.365, 'text': 'Well, two common types of machine learning that we actually saw in lecture one and two yesterday are those of classification and those of regression.', 'start': 265.179, 'duration': 8.186}, {'end': 277.127, 'text': 'In regression, we have our output take a continuous value.', 'start': 273.545, 'duration': 3.582}], 'summary': 'Images can be represented as matrices of numbers, allowing for tasks like classification and regression in computer vision.', 'duration': 45.374, 'max_score': 231.753, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI231753.jpg'}, {'end': 420.091, 'src': 'embed', 'start': 391.564, 'weight': 3, 'content': [{'end': 394.987, 'text': 'And remember that these images are just three-dimensional arrays of numbers.', 'start': 391.564, 'duration': 3.423}, {'end': 402.16, 'text': "well, actually they're just three-dimensional rays of brightness values, and that images can hold tons of variation.", 'start': 396.716, 'duration': 5.444}, {'end': 409.004, 'text': "So there's variation such as occlusions that we have to deal with, variations in illumination, and even inter-class variation.", 'start': 402.54, 'duration': 6.464}, {'end': 420.091, 'text': "And when we're building our classification pipeline, we need to be invariant to all of these variations, and be sensitive to inter-class variation.", 'start': 409.705, 'duration': 10.386}], 'summary': 'Images are three-dimensional arrays of brightness values with variations such as occlusions, illumination, and inter-class variation, requiring invariance and sensitivity in classification pipeline.', 'duration': 28.527, 'max_score': 391.564, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI391564.jpg'}], 'start': 188.557, 'title': 'Computer vision basics and image classification', 'summary': 'Explores the fundamental elements of computer vision, including how computers process images and differentiate between grayscale and rgb images. it also addresses the foundation for image classification in computer vision, highlighting the common types of machine learning, challenges in classification, and the need for sensitivity to inter-class variation.', 'chapters': [{'end': 252.981, 'start': 188.557, 'title': 'Computer vision basics', 'summary': 'Explores how computers process images, representing them as matrices of numbers and differentiating between grayscale and rgb images, providing insight into the fundamental elements of computer vision.', 'duration': 64.424, 'highlights': ['Computers process images by representing them as two-dimensional matrices of numbers, with grayscale images being represented by a single number for each pixel and RGB images being represented as three two-dimensional matrices for the red, green, and blue channels.', 'Understanding the fundamental process of how computers see images helps in grasping the basis of computer vision tasks, broadening our understanding of training machines to process visual data.']}, {'end': 433.146, 'start': 254.042, 'title': 'Image classification in computer vision', 'summary': 'Discusses the foundation for image classification in computer vision, addressing the common types of machine learning, the challenges in classification, and the need for sensitivity to inter-class variation.', 'duration': 179.104, 'highlights': ['The chapter discusses the foundation for image classification in computer vision', 'The chapter addresses the common types of machine learning - classification and regression', 'The chapter emphasizes the need for sensitivity to inter-class variation in image classification']}], 'duration': 244.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI188557.jpg', 'highlights': ['Computers process images by representing them as two-dimensional matrices of numbers, with grayscale images being represented by a single number for each pixel and RGB images being represented as three two-dimensional matrices for the red, green, and blue channels.', 'Understanding the fundamental process of how computers see images helps in grasping the basis of computer vision tasks, broadening our understanding of training machines to process visual data.', 'The chapter discusses the foundation for image classification in computer vision', 'The chapter emphasizes the need for sensitivity to inter-class variation in image classification', 'The chapter addresses the common types of machine learning - classification and regression']}, {'end': 870.865, 'segs': [{'end': 471.414, 'src': 'embed', 'start': 433.146, 'weight': 1, 'content': [{'end': 436.568, 'text': 'the manual extraction of those features is where this really breaks down.', 'start': 433.146, 'duration': 3.422}, {'end': 441.912, 'text': 'Now, due to the incredible variability in image data specifically,', 'start': 437.669, 'duration': 4.243}, {'end': 448.976, 'text': 'the detection of these features is super difficult in practice and manually extracting these features can be extremely brittle.', 'start': 441.912, 'duration': 7.064}, {'end': 454.339, 'text': "So how can we do better than this? That's really the question that we want to tackle today.", 'start': 450.156, 'duration': 4.183}, {'end': 465.033, 'text': 'One way is that we want to extract both these visual features and detect their presence in the image simultaneously and in a hierarchical fashion.', 'start': 455.911, 'duration': 9.122}, {'end': 471.414, 'text': 'And for that, we can use neural networks like we saw in class number one and two.', 'start': 466.993, 'duration': 4.421}], 'summary': 'Manually extracting features from image data is difficult and brittle. neural networks offer a better approach for simultaneous feature extraction and detection.', 'duration': 38.268, 'max_score': 433.146, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI433146.jpg'}, {'end': 581.119, 'src': 'embed', 'start': 572.395, 'weight': 5, 'content': [{'end': 578.597, 'text': "Because it's densely connected, we're connecting every single pixel in our input to every single neuron in our hidden layer.", 'start': 572.395, 'duration': 6.202}, {'end': 581.119, 'text': 'So this is not really feasible in practice.', 'start': 579.477, 'duration': 1.642}], 'summary': 'Connecting every pixel to every neuron is not feasible.', 'duration': 8.724, 'max_score': 572.395, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI572395.jpg'}, {'end': 693.934, 'src': 'embed', 'start': 669.984, 'weight': 0, 'content': [{'end': 677.026, 'text': 'But we also remember that the final task that we really want to do here, that I told you we wanted to do, was to learn visual features.', 'start': 669.984, 'duration': 7.042}, {'end': 682.528, 'text': 'And we can do this very simply by weighting those connections in the patches.', 'start': 677.867, 'duration': 4.661}, {'end': 684.389, 'text': 'So each of the patches.', 'start': 682.968, 'duration': 1.421}, {'end': 692.853, 'text': "instead of just connecting them uniformly to our hidden layer, we're going to weight each of those pixels and apply a similar technique,", 'start': 684.389, 'duration': 8.464}, {'end': 693.934, 'text': 'like we saw in lab one.', 'start': 692.853, 'duration': 1.081}], 'summary': 'The final task is to learn visual features by weighting connections in patches.', 'duration': 23.95, 'max_score': 669.984, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI669984.jpg'}, {'end': 771.049, 'src': 'embed', 'start': 745.032, 'weight': 2, 'content': [{'end': 749.473, 'text': 'We shift it, for example, in units of two pixels each time to grab the next patch.', 'start': 745.032, 'duration': 4.441}, {'end': 751.473, 'text': 'We repeat the convolution operation.', 'start': 749.793, 'duration': 1.68}, {'end': 755.494, 'text': "And that's how we can start to think about extracting features in our input.", 'start': 752.093, 'duration': 3.401}, {'end': 762.024, 'text': "But you're probably wondering how does this convolution operation actually relate to feature extraction?", 'start': 757.401, 'duration': 4.623}, {'end': 766.747, 'text': "So so far we've just defined the sliding operation where we can slide a patch over the input,", 'start': 762.144, 'duration': 4.603}, {'end': 771.049, 'text': "but we haven't really talked about how that allows us to extract features from that image itself.", 'start': 766.747, 'duration': 4.302}], 'summary': 'Sliding patch by 2 pixels for repeat convolution to extract features.', 'duration': 26.017, 'max_score': 745.032, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI745032.jpg'}, {'end': 856.914, 'src': 'embed', 'start': 829.967, 'weight': 3, 'content': [{'end': 834.268, 'text': "If they share a lot of the same visual features, then they're probably representing the same object.", 'start': 829.967, 'duration': 4.301}, {'end': 838.996, 'text': 'Now, each feature is like a mini image.', 'start': 837.014, 'duration': 1.982}, {'end': 840.898, 'text': 'Each of these patches is like a mini image.', 'start': 839.216, 'duration': 1.682}, {'end': 842.82, 'text': "It's also a two-dimensional array of numbers.", 'start': 840.978, 'duration': 1.842}, {'end': 851.369, 'text': "And we'll use these filters, let me call them now, to pick up on the features common to X.", 'start': 843.981, 'duration': 7.388}, {'end': 856.914, 'text': 'In the case of Xs, filters representing diagonal lines and crosses are probably the most important things to look for.', 'start': 851.369, 'duration': 5.545}], 'summary': 'Using filters to identify visual features representing objects.', 'duration': 26.947, 'max_score': 829.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI829967.jpg'}], 'start': 433.146, 'title': 'Learning visual features and spatial structure in neural networks', 'summary': 'Covers learning visual features using neural networks in a hierarchical fashion, addressing limitations of densely connected networks for image classification. it also discusses implementing spatial structure in neural networks through patch-based connections and weighted summation to extract visual features, with a focus on feature extraction in convolutional neural networks.', 'chapters': [{'end': 581.119, 'start': 433.146, 'title': 'Learning visual features with neural networks', 'summary': 'Discusses the challenges of manually extracting visual features from image data, proposes learning visual features using neural networks in a hierarchical fashion, and highlights the limitations of using densely connected networks for image classification.', 'duration': 147.973, 'highlights': ['Learning visual features using neural networks in a hierarchical fashion', 'Challenges of manually extracting visual features from image data', 'Limitations of using densely connected networks for image classification']}, {'end': 870.865, 'start': 582.16, 'title': 'Spatial structure in convolutional neural networks', 'summary': 'Discusses the importance of spatial structure in image data and introduces the concept of building spatial structure into neural networks using patch-based connections and weighted summation to extract visual features, leading to the development of convolutional neural networks, with a focus on feature extraction.', 'duration': 288.705, 'highlights': ['Introduction of patch-based connections and weighted summation to maintain spatial structure and extract visual features', 'Explanation of the convolution operation and its role in feature extraction', 'Importance of feature matching and the role of filters in identifying visual features']}], 'duration': 437.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI433146.jpg', 'highlights': ['Introduction of patch-based connections and weighted summation to maintain spatial structure and extract visual features', 'Learning visual features using neural networks in a hierarchical fashion', 'Explanation of the convolution operation and its role in feature extraction', 'Importance of feature matching and the role of filters in identifying visual features', 'Challenges of manually extracting visual features from image data', 'Limitations of using densely connected networks for image classification']}, {'end': 1111.957, 'segs': [{'end': 910.726, 'src': 'embed', 'start': 871.406, 'weight': 0, 'content': [{'end': 874.208, 'text': 'And note that the smaller matrices are the filters of weights.', 'start': 871.406, 'duration': 2.802}, {'end': 879.452, 'text': 'So these are the actual values of the weights that correspond to that patch as we slide it across the image.', 'start': 874.508, 'duration': 4.944}, {'end': 887.258, 'text': "Now all that's left to do here is really just define that convolution operation and tell you, when you slide that patch over the image,", 'start': 880.392, 'duration': 6.866}, {'end': 895.331, 'text': 'What is the actual operation that takes that patch on top of that image and then produces that next output at the hidden neuron layer?', 'start': 888.345, 'duration': 6.986}, {'end': 904.019, 'text': 'So convolution preserves that spatial structure between pixels by learning the image features in these small squares or these small patches of the input data.', 'start': 896.052, 'duration': 7.967}, {'end': 910.726, 'text': 'To do this, the entire equation or the entire computation is as follows.', 'start': 904.46, 'duration': 6.266}], 'summary': 'Convolutional neural networks learn image features through sliding filters over the image, preserving spatial structure between pixels.', 'duration': 39.32, 'max_score': 871.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI871406.jpg'}, {'end': 964.998, 'src': 'embed', 'start': 934.124, 'weight': 1, 'content': [{'end': 937.605, 'text': 'The result you can see on the right is just a matrix of all ones,', 'start': 934.124, 'duration': 3.481}, {'end': 942.787, 'text': "because there's perfect overlap between our filter in this case and our image at the patch location.", 'start': 937.605, 'duration': 5.182}, {'end': 951.51, 'text': "The only thing left to do here is sum up all of those numbers and when you sum them up you get nine and that's the output at the next layer.", 'start': 944.507, 'duration': 7.003}, {'end': 959.713, 'text': "Let's go through one more example, a little bit more slowly, of how we did this,", 'start': 953.968, 'duration': 5.745}, {'end': 964.998, 'text': 'and you might be able to appreciate what this convolution operation is intuitively telling us now.', 'start': 959.713, 'duration': 5.285}], 'summary': 'Convolution operation results in a matrix of all ones, with a sum of nine as the output at the next layer.', 'duration': 30.874, 'max_score': 934.124, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI934124.jpg'}, {'end': 1050.521, 'src': 'embed', 'start': 979.906, 'weight': 2, 'content': [{'end': 989.673, 'text': 'we need to cover that entire image by sliding the filter over that image and performing that element-wise multiplication and adding the output for each patch.', 'start': 979.906, 'duration': 9.767}, {'end': 991.454, 'text': 'And this is what that looks like.', 'start': 990.393, 'duration': 1.061}, {'end': 995.417, 'text': "So first, we'll start off by placing that yellow filter on the top left corner.", 'start': 992.174, 'duration': 3.243}, {'end': 999.86, 'text': "We're going to element-wise multiply and add all of the outputs, and we're going to get four.", 'start': 995.977, 'duration': 3.883}, {'end': 1004.563, 'text': "And we're going to place that four in our first entry of our output matrix.", 'start': 1000.5, 'duration': 4.063}, {'end': 1006.424, 'text': 'This is called the feature map.', 'start': 1005.223, 'duration': 1.201}, {'end': 1013.945, 'text': 'Now we can continue this and slide that three by three filter over the image element wise multiply,', 'start': 1008.12, 'duration': 5.825}, {'end': 1019.811, 'text': 'add up all the numbers and place the next result in the next row, in the next column, which is three.', 'start': 1013.945, 'duration': 5.866}, {'end': 1025.113, 'text': "And we can just keep repeating this operation over and over And that's it.", 'start': 1020.752, 'duration': 4.361}, {'end': 1032.856, 'text': 'The feature map on the right reflects where in the image there is activation by this particular filter.', 'start': 1025.252, 'duration': 7.604}, {'end': 1035.057, 'text': "So let's take a look at this filter really quickly.", 'start': 1033.296, 'duration': 1.761}, {'end': 1038.318, 'text': 'You can see in this filter, this filter is an X or a cross.', 'start': 1035.337, 'duration': 2.981}, {'end': 1040.339, 'text': 'It has ones on both diagonals.', 'start': 1038.778, 'duration': 1.561}, {'end': 1046.501, 'text': "And in the image, you can see that it's being activated also along this main diagonal.", 'start': 1040.999, 'duration': 5.502}, {'end': 1050.521, 'text': 'on the four, where the four is being maximally activated.', 'start': 1047.579, 'duration': 2.942}], 'summary': 'Sliding filter over image, element-wise multiplication, and adding outputs to generate feature map for activation.', 'duration': 70.615, 'max_score': 979.906, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI979906.jpg'}, {'end': 1094.563, 'src': 'embed', 'start': 1068.847, 'weight': 4, 'content': [{'end': 1076.41, 'text': "So simply by changing the weights in your filter, you can change what your filter is looking for or what it's going to be activating.", 'start': 1068.847, 'duration': 7.563}, {'end': 1081.373, 'text': 'So take for example this image of this woman Lena on the left.', 'start': 1077.21, 'duration': 4.163}, {'end': 1083.294, 'text': "That's the original image on the left.", 'start': 1081.873, 'duration': 1.421}, {'end': 1088.839, 'text': 'If you slide different filters over this image you can get different output feature maps.', 'start': 1083.375, 'duration': 5.464}, {'end': 1094.563, 'text': 'So for example you can sharpen this image by having a filter shown on the second column.', 'start': 1089.339, 'duration': 5.224}], 'summary': 'Adjusting filter weights changes filter function and output feature maps.', 'duration': 25.716, 'max_score': 1068.847, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1068847.jpg'}], 'start': 871.406, 'title': 'Convolutional neural networks', 'summary': 'Covers the convolution operation in neural networks, image filtering, and feature mapping to produce feature maps, and the activation of filters in convolutional neural networks, with specific examples and their impact on detecting different features.', 'chapters': [{'end': 979.906, 'start': 871.406, 'title': 'Convolution operation in neural networks', 'summary': 'Explains the convolution operation in neural networks, which preserves spatial structure and learns image features by sliding small patches of input data, performing element-wise multiplication, and summing up the results to produce the output at the hidden neuron layer.', 'duration': 108.5, 'highlights': ['Convolution preserves spatial structure and learns image features by sliding small patches of input data, performing element-wise multiplication, and summing up the results to produce the output at the hidden neuron layer.', 'The result of the element-wise multiplication is a matrix of all ones, indicating perfect overlap between the filter and the image at the patch location, resulting in an output of nine at the next layer.', 'The smaller matrices are the filters of weights, representing the actual values of the weights that correspond to the patches as they slide across the image.']}, {'end': 1025.113, 'start': 979.906, 'title': 'Image filtering and feature mapping', 'summary': 'Explains the process of sliding a filter over an image, performing element-wise multiplication, and adding the output for each patch, resulting in feature mapping, with a specific example resulting in a 3x3 feature map.', 'duration': 45.207, 'highlights': ['Sliding a filter over an image, performing element-wise multiplication, and adding the output for each patch results in feature mapping, as demonstrated with a specific example resulting in a 3x3 feature map.', 'Placing the yellow filter on the top left corner and element-wise multiplying and adding all the outputs results in the value four, placed in the first entry of the output matrix.', 'Continuing the operation by sliding the three by three filter over the image, element-wise multiplying, adding up all the numbers, and placing the next result in the next row and column results in the value three being placed in the next entry of the output matrix.']}, {'end': 1111.957, 'start': 1025.252, 'title': 'Convolutional neural networks', 'summary': 'Explains how filters in a convolutional neural network are activated based on the image features, demonstrating the impact of filter weights on detecting different features, with examples of sharpening and edge detection.', 'duration': 86.705, 'highlights': ['The filter with ones on both diagonals is maximally activated along the central diagonal of the image, demonstrating maximum overlap (quantifiable data: visual representation of filter activation).', 'Changing the weights in a filter can result in different output feature maps, such as sharpening the image and detecting edges, illustrating the impact of filter weights on feature detection (quantifiable data: examples of different filter outputs).']}], 'duration': 240.551, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI871406.jpg', 'highlights': ['Convolution preserves spatial structure and learns image features by sliding small patches of input data, performing element-wise multiplication, and summing up the results', 'The result of the element-wise multiplication is a matrix of all ones, indicating perfect overlap between the filter and the image at the patch location, resulting in an output of nine at the next layer', 'Sliding a filter over an image, performing element-wise multiplication, and adding the output for each patch results in feature mapping, as demonstrated with a specific example resulting in a 3x3 feature map', 'The filter with ones on both diagonals is maximally activated along the central diagonal of the image, demonstrating maximum overlap (quantifiable data: visual representation of filter activation)', 'Changing the weights in a filter can result in different output feature maps, such as sharpening the image and detecting edges, illustrating the impact of filter weights on feature detection (quantifiable data: examples of different filter outputs)', 'The smaller matrices are the filters of weights, representing the actual values of the weights that correspond to the patches as they slide across the image', 'Placing the yellow filter on the top left corner and element-wise multiplying and adding all the outputs results in the value four, placed in the first entry of the output matrix', 'Continuing the operation by sliding the three by three filter over the image, element-wise multiplying, adding up all the numbers, and placing the next result in the next row and column results in the value three being placed in the next entry of the output matrix']}, {'end': 1565.678, 'segs': [{'end': 1139.007, 'src': 'embed', 'start': 1115.098, 'weight': 2, 'content': [{'end': 1125.103, 'text': 'So now I hope you can appreciate how convolution allows us to capitalize on spatial structure and use sets of weights to extract these local features within images.', 'start': 1115.098, 'duration': 10.005}, {'end': 1132.322, 'text': 'And very easily we can detect different features by simply changing our weights and using different filters.', 'start': 1126.098, 'duration': 6.224}, {'end': 1139.007, 'text': 'OK, Now these concepts of preserving spatial information and spatial structure,', 'start': 1132.903, 'duration': 6.104}], 'summary': 'Convolution allows for extracting local features in images.', 'duration': 23.909, 'max_score': 1115.098, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1115098.jpg'}, {'end': 1233.407, 'src': 'heatmap', 'start': 1163.753, 'weight': 0, 'content': [{'end': 1169.837, 'text': 'And these networks are very appropriately named convolutional neural networks because the backbone of them is the convolution operation.', 'start': 1163.753, 'duration': 6.084}, {'end': 1177.367, 'text': "And we'll take a look first at a CNN, or Convolutional Neural Network, architecture designed for image classification tasks.", 'start': 1170.778, 'duration': 6.589}, {'end': 1185.358, 'text': "And we'll see how the convolution operation can actually feed into those spatial sampling operations so that we can build this full thing end-to-end.", 'start': 1177.988, 'duration': 7.37}, {'end': 1193.23, 'text': "So first let's consider the simple, very simple CNN for image classification.", 'start': 1188.587, 'duration': 4.643}, {'end': 1200.635, 'text': 'Now here the goal is to learn features directly from data and to use these learn feature maps for classification of these images.', 'start': 1193.49, 'duration': 7.145}, {'end': 1205.979, 'text': 'There are three main parts to a CNN that I want to talk about now.', 'start': 1201.996, 'duration': 3.983}, {'end': 1208.981, 'text': "First part is the convolutions, which we've talked about before.", 'start': 1206.539, 'duration': 2.442}, {'end': 1213.944, 'text': 'These are for extracting the features in your image or in your previous layer in a more generic saying.', 'start': 1209.021, 'duration': 4.923}, {'end': 1218.022, 'text': 'The second step is applying your non-linearity.', 'start': 1215.421, 'duration': 2.601}, {'end': 1219.522, 'text': 'And again, like we saw in lecture,', 'start': 1218.282, 'duration': 1.24}, {'end': 1227.725, 'text': 'one and two non-linearities allow us to deal with non-linear data and introduce complexity into our learning pipeline so that we can solve these more complex tasks.', 'start': 1219.522, 'duration': 8.203}, {'end': 1233.407, 'text': 'And finally, the third step, which is what I was talking about before, is this pooling operation,', 'start': 1228.645, 'duration': 4.762}], 'summary': 'Convolutional neural networks (cnn) use convolution, non-linearity, and pooling to extract features and classify images.', 'duration': 55.769, 'max_score': 1163.753, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1163753.jpg'}, {'end': 1387.044, 'src': 'embed', 'start': 1361.531, 'weight': 4, 'content': [{'end': 1369.495, 'text': 'But with a single convolutional layer, we can have multiple different filters or multiple different features that we might want to extract or detect.', 'start': 1361.531, 'duration': 7.964}, {'end': 1375.321, 'text': 'The output layer of a convolution, therefore, is not a single image as well,', 'start': 1370.7, 'duration': 4.621}, {'end': 1379.962, 'text': 'but rather a volume of images representing all of the different filters that it detects.', 'start': 1375.321, 'duration': 4.641}, {'end': 1387.044, 'text': "So here D, the depth, is the number of filters or the number of features that you want to detect in that image, and that's set by the human.", 'start': 1379.982, 'duration': 7.062}], 'summary': 'A single convolutional layer can have multiple filters or features, creating a volume of images based on the desired number set by humans.', 'duration': 25.513, 'max_score': 1361.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1361531.jpg'}, {'end': 1489.239, 'src': 'embed', 'start': 1462.287, 'weight': 5, 'content': [{'end': 1465.608, 'text': 'Now here the ReLU activation function, rectified linear unit.', 'start': 1462.287, 'duration': 3.321}, {'end': 1466.869, 'text': "we haven't talked about it yet,", 'start': 1465.608, 'duration': 1.261}, {'end': 1478.314, 'text': 'but this is just an activation function that takes as input any real number and essentially shifts everything less than zero to zero and anything greater than zero.', 'start': 1466.869, 'duration': 11.445}, {'end': 1478.994, 'text': 'it keeps the same.', 'start': 1478.314, 'duration': 0.68}, {'end': 1484.517, 'text': 'Another way you can think about this is it makes sure that the minimum of whatever you feed in is zero.', 'start': 1479.735, 'duration': 4.782}, {'end': 1486.478, 'text': "So if it's greater than zero, it doesn't touch it.", 'start': 1484.857, 'duration': 1.621}, {'end': 1489.239, 'text': "If it's less than zero, it makes sure that it caps it at zero.", 'start': 1486.858, 'duration': 2.381}], 'summary': 'Relu activation function shifts negative inputs to zero, keeps positive inputs unchanged.', 'duration': 26.952, 'max_score': 1462.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1462287.jpg'}, {'end': 1525.351, 'src': 'embed', 'start': 1503.237, 'weight': 6, 'content': [{'end': 1513.463, 'text': 'Now the pooling operation is used to reduce the dimensionality of our input layers, and this can be done on any layer after the convolutional layer.', 'start': 1503.237, 'duration': 10.226}, {'end': 1516.365, 'text': 'So you can apply on your input image a convolutional layer,', 'start': 1513.504, 'duration': 2.861}, {'end': 1525.351, 'text': 'apply a non-linearity and then downsample using a pooling layer to get a different spatial resolution before applying your next convolutional layer,', 'start': 1516.365, 'duration': 8.986}], 'summary': 'Pooling operation reduces input layer dimensionality, applicable after convolutional layer.', 'duration': 22.114, 'max_score': 1503.237, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1503237.jpg'}], 'start': 1115.098, 'title': 'Cnn basics', 'summary': 'Covers the core concepts of convolutional neural networks, including the use of convolution to extract local features, spatial sampling operations, basic cnn architecture with convolution operation, local connectivity, relu activation, and pooling operation.', 'chapters': [{'end': 1233.407, 'start': 1115.098, 'title': 'Understanding convolutional neural networks', 'summary': 'Discusses the core concepts of convolutional neural networks, highlighting the use of convolution to extract local features within images and how it feeds into spatial sampling operations to build end-to-end cnns for computer vision tasks.', 'duration': 118.309, 'highlights': ['Convolution allows us to capitalize on spatial structure and use sets of weights to extract local features within images.', 'CNNs are named after the convolution operation and are used for computer vision tasks, with the backbone being the convolution operation.', 'The goal of a CNN for image classification is to learn features directly from data and use these learned feature maps for classification.', 'The three main parts of a CNN are convolutions for feature extraction, non-linearity application for dealing with non-linear data, and the pooling operation.']}, {'end': 1565.678, 'start': 1233.407, 'title': 'Convolutional neural networks', 'summary': 'Explains the basic architecture of a cnn, including the convolution operation, local connectivity, the relu activation function, and the pooling operation, which allows for downsampling and spatial invariance in image processing.', 'duration': 332.271, 'highlights': ['The output layer of a convolution is a volume of images representing all of the different filters that it detects, with D, the depth, being the number of features set by the human.', 'The ReLU activation function helps in dealing with highly non-linear data by shifting everything less than zero to zero and keeping anything greater than zero the same.', 'The pooling operation, such as max pooling, is used to reduce the dimensionality of input layers and can be applied after the convolutional layer to downsample the spatial resolution of the image.']}], 'duration': 450.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1115098.jpg', 'highlights': ['The goal of a CNN for image classification is to learn features directly from data and use these learned feature maps for classification.', 'CNNs are named after the convolution operation and are used for computer vision tasks, with the backbone being the convolution operation.', 'Convolution allows us to capitalize on spatial structure and use sets of weights to extract local features within images.', 'The three main parts of a CNN are convolutions for feature extraction, non-linearity application for dealing with non-linear data, and the pooling operation.', 'The output layer of a convolution is a volume of images representing all of the different filters that it detects, with D, the depth, being the number of features set by the human.', 'The ReLU activation function helps in dealing with highly non-linear data by shifting everything less than zero to zero and keeping anything greater than zero the same.', 'The pooling operation, such as max pooling, is used to reduce the dimensionality of input layers and can be applied after the convolutional layer to downsample the spatial resolution of the image.']}, {'end': 1942.613, 'segs': [{'end': 1591.776, 'src': 'embed', 'start': 1565.998, 'weight': 5, 'content': [{'end': 1573.48, 'text': 'This makes us, or this allows us to shrink the spatial dimensions of our image while still maintaining all of that spatial structure.', 'start': 1565.998, 'duration': 7.482}, {'end': 1584.473, 'text': 'So actually, this is a great point because I encourage all of you to think about what are some other ways that you could perform a pooling operation.', 'start': 1575.99, 'duration': 8.483}, {'end': 1587.895, 'text': 'How else could you downsample these images? Max pooling is one way.', 'start': 1584.533, 'duration': 3.362}, {'end': 1591.776, 'text': 'So you could always take the maximum of these two by two patches.', 'start': 1587.955, 'duration': 3.821}], 'summary': 'Max pooling allows downsampling of images by taking maximum of 2x2 patches.', 'duration': 25.778, 'max_score': 1565.998, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1565998.jpg'}, {'end': 1677.118, 'src': 'embed', 'start': 1649.421, 'weight': 4, 'content': [{'end': 1653.284, 'text': "So that's where we want to extract those features and learn the features from our image data.", 'start': 1649.421, 'duration': 3.863}, {'end': 1656.947, 'text': 'This is simply applying that same idea that I showed you before.', 'start': 1653.965, 'duration': 2.982}, {'end': 1658.028, 'text': "We're going to stack.", 'start': 1656.987, 'duration': 1.041}, {'end': 1665.729, 'text': 'convolution and nonlinearities with pooling operations and repeat this throughout the depth of our network.', 'start': 1659.625, 'duration': 6.104}, {'end': 1677.118, 'text': 'The next step for our convolutional neural network is to take those extracted or learned features and to classify our image right?', 'start': 1667.47, 'duration': 9.648}], 'summary': 'Extract features from image data using convolutional neural network.', 'duration': 27.697, 'max_score': 1649.421, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1649421.jpg'}, {'end': 1809.079, 'src': 'embed', 'start': 1777.811, 'weight': 0, 'content': [{'end': 1784.202, 'text': 'In reality, this architecture extends to many, many different types of tasks and many, many different types of applications as well.', 'start': 1777.811, 'duration': 6.391}, {'end': 1790.25, 'text': "When we're considering CNNs for classification, we saw that it has two main parts,", 'start': 1785.748, 'duration': 4.502}, {'end': 1797.413, 'text': 'first being the feature learning part shown here and then a classification part on the second part of the pipeline.', 'start': 1790.25, 'duration': 7.163}, {'end': 1809.079, 'text': 'What makes a convolutional neural network so powerful is that you can take this feature extraction part of the pipeline and at the output you can attach whatever kind of output that you want to it.', 'start': 1797.934, 'duration': 11.145}], 'summary': 'Cnns extend to various tasks and applications, with feature learning and classification parts, making them powerful.', 'duration': 31.268, 'max_score': 1777.811, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1777811.jpg'}, {'end': 1851.608, 'src': 'embed', 'start': 1817.784, 'weight': 3, 'content': [{'end': 1820.266, 'text': 'So you can do detection by changing the output head.', 'start': 1817.784, 'duration': 2.482}, {'end': 1827.472, 'text': 'You can do semantic segmentation, which is where you want to detect semantic classes for every pixel in your image.', 'start': 1820.566, 'duration': 6.906}, {'end': 1831.396, 'text': 'You can also do end-to-end robotic control like we saw with autonomous driving before.', 'start': 1828.113, 'duration': 3.283}, {'end': 1840.818, 'text': "So what's an example of this? We've seen a significant impact in computer vision in medicine and healthcare over the last couple years.", 'start': 1833.891, 'duration': 6.927}, {'end': 1842.639, 'text': 'Just a couple weeks ago.', 'start': 1840.938, 'duration': 1.701}, {'end': 1851.608, 'text': 'actually there was this paper that came out, where deep learning models have been applied to the analysis of a whole host of breast.', 'start': 1842.639, 'duration': 8.969}], 'summary': 'Deep learning applications in computer vision have impacted medicine and healthcare, with significant progress in breast analysis.', 'duration': 33.824, 'max_score': 1817.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1817784.jpg'}, {'end': 1881.168, 'src': 'embed', 'start': 1859.865, 'weight': 1, 'content': [{'end': 1869.335, 'text': 'So what was showed here was that CNNs were able to significantly outperform expert radiologists in detecting breast cancer directly from these mammogram images.', 'start': 1859.865, 'duration': 9.47}, {'end': 1877.003, 'text': "That's done by feeding these images through a convolutional feature, extractor, outputting those features, those learned features,", 'start': 1869.796, 'duration': 7.207}, {'end': 1881.168, 'text': 'to dense layers and then performing classification based on those dense layers.', 'start': 1877.003, 'duration': 4.165}], 'summary': 'Cnns outperformed radiologists in detecting breast cancer from mammogram images.', 'duration': 21.303, 'max_score': 1859.865, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1859865.jpg'}, {'end': 1923.23, 'src': 'embed', 'start': 1901.132, 'weight': 6, 'content': [{'end': 1909.215, 'text': "And then they're upscaled through the inverse convolutional decoder to predict for every pixel in that image what is the class of that pixel.", 'start': 1901.132, 'duration': 8.083}, {'end': 1919.025, 'text': 'So you can see that the network is able to correctly classify that it sees two cows in brown, whereas the grass is in green and the sky is in blue.', 'start': 1909.235, 'duration': 9.79}, {'end': 1923.23, 'text': 'And this is basically detection, but not for a single number over the image.', 'start': 1919.405, 'duration': 3.825}], 'summary': 'Neural network predicts pixel classes; detects two cows in brown, grass in green, and sky in blue.', 'duration': 22.098, 'max_score': 1901.132, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1901132.jpg'}], 'start': 1565.998, 'title': 'Cnn in image analysis', 'summary': 'Covers feature extraction and classification in convolutional neural networks, including convolution, pooling, and fully connected layers, emphasizing downsampling images. it also explains the process of building an end-to-end cnn and its impact in medical image analysis and semantic segmentation.', 'chapters': [{'end': 1708.118, 'start': 1565.998, 'title': 'Cnn feature learning and classification', 'summary': 'Covers the concept of feature extraction and classification in convolutional neural networks, emphasizing the use of convolution, pooling operations, and fully connected layers, with a focus on exploring different ways of downsampling images.', 'duration': 142.12, 'highlights': ['CNNs use convolution, pooling, and fully connected layers for feature extraction and classification.', 'Exploring various methods for downsampling images is encouraged for enhancing understanding.', 'Stacking convolution and nonlinearities with pooling operations is employed for feature learning.']}, {'end': 1942.613, 'start': 1710.68, 'title': 'Building a convolutional neural network', 'summary': 'Explains how to build an end-to-end convolutional neural network, featuring the process of feature extraction, classification, and its applications, highlighted by the significant impact in medical image analysis and semantic segmentation.', 'duration': 231.933, 'highlights': ['The significant impact of CNNs in medical image analysis, where CNNs outperformed expert radiologists in detecting breast cancer directly from mammogram images.', 'The versatility of CNNs, as they can be used for tasks like semantic segmentation, end-to-end robotic control, and image classification, extending beyond just image classification.', 'The process of feature extraction in CNNs involves down sampling spatial information using max pooling and extracting features through convolutional layers, ultimately allowing for the classification of images into different classes.', 'The ability of CNNs to predict the class of each pixel in an image, showcasing their capability for semantic segmentation and solving more complex classification problems.']}], 'duration': 376.615, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1565998.jpg', 'highlights': ['CNNs use convolution, pooling, and fully connected layers for feature extraction and classification.', 'The significant impact of CNNs in medical image analysis, outperforming expert radiologists in detecting breast cancer.', 'The process of building an end-to-end CNN and its impact in medical image analysis and semantic segmentation.', 'The versatility of CNNs, extending beyond just image classification to tasks like semantic segmentation and end-to-end robotic control.', 'Stacking convolution and nonlinearities with pooling operations is employed for feature learning.', 'Exploring various methods for downsampling images is encouraged for enhancing understanding.', 'The ability of CNNs to predict the class of each pixel in an image, showcasing their capability for semantic segmentation and solving more complex classification problems.']}, {'end': 2234.262, 'segs': [{'end': 1968.094, 'src': 'embed', 'start': 1942.613, 'weight': 0, 'content': [{'end': 1950.596, 'text': 'which scale back up our image data and allow us to predict these images as outputs and not just single numbers or single probability distributions.', 'start': 1942.613, 'duration': 7.983}, {'end': 1960.469, 'text': 'And of course this idea can be, you can imagine, very easily applied to many other applications in health care as well,', 'start': 1952.745, 'duration': 7.724}, {'end': 1968.094, 'text': "especially for segmenting various types of cancers, such as here we're showing brain tumors on the top,", 'start': 1960.469, 'duration': 7.625}], 'summary': 'Scaling up image data for predicting outputs, applicable in healthcare, including segmenting brain tumors.', 'duration': 25.481, 'max_score': 1942.613, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1942613.jpg'}, {'end': 2177.443, 'src': 'embed', 'start': 2132.104, 'weight': 1, 'content': [{'end': 2136.708, 'text': 'given what it sees on the road, to actually actuate that vehicle towards that destination.', 'start': 2132.104, 'duration': 4.604}, {'end': 2146.869, 'text': "Note here that the vehicle is able to successfully navigate through those intersections even though it's never been driving in this area before,", 'start': 2138.359, 'duration': 8.51}, {'end': 2151.394, 'text': "it's never seen these roads before, and we never even told it what an intersection was.", 'start': 2146.869, 'duration': 4.525}, {'end': 2154.478, 'text': 'It learned all of this from data using convolutional neural networks.', 'start': 2151.594, 'duration': 2.884}, {'end': 2162.297, 'text': "Now, the impact of CNNs has been very, very wide reaching beyond these examples that I've given to you today.", 'start': 2156.055, 'duration': 6.242}, {'end': 2172.001, 'text': 'And it has touched so many different fields of computer vision, ranging across robotics, medicine, and many, many other fields.', 'start': 2163.178, 'duration': 8.823}, {'end': 2177.443, 'text': "I'd like to conclude by taking a look at what we've covered in today's lecture.", 'start': 2173.342, 'duration': 4.101}], 'summary': 'Convolutional neural networks enable vehicle to navigate intersections and have wide-reaching impact in fields such as robotics and medicine.', 'duration': 45.339, 'max_score': 2132.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI2132104.jpg'}], 'start': 1942.613, 'title': 'Neural networks in healthcare, self-driving cars, and computer vision', 'summary': 'Covers the application of neural networks in healthcare for segmenting brain tumors and infected blood, self-driving cars for autonomous navigation, and the impact of convolutional neural networks in computer vision and various fields.', 'chapters': [{'end': 1991.756, 'start': 1942.613, 'title': 'Neural networks in healthcare and self-driving cars', 'summary': 'Discusses the application of neural networks in healthcare, specifically for segmenting brain tumors and parts of the blood infected with malaria, as well as in self-driving cars for learning autonomous navigation.', 'duration': 49.143, 'highlights': ['Neural networks are used in healthcare to segment brain tumors and parts of the blood infected with malaria, allowing for more accurate predictions (quantifiable data: accuracy improvement).', 'The application of neural networks in self-driving cars enables the learning of autonomous navigation, showcasing their versatility in different domains (quantifiable data: improved navigation accuracy).']}, {'end': 2094.315, 'start': 1992.916, 'title': 'Neural network for autonomous car control', 'summary': "Discusses using a neural network model to process images from a car's camera and bird's eye view to predict a distribution of possible control actions, allowing the car to steer in different directions based on its perception of the world.", 'duration': 101.399, 'highlights': ["The neural network processes images from the car's camera and bird's eye view to predict a distribution of possible control actions, enabling the car to steer in different directions without a specific goal destination in mind.", 'The model concatenates learned features from the sensor data to infer control outputs, enabling a global set of features across all sensor data to be utilized for predicting car control actions.', 'The network is trained end-to-end, with convolutional encoders or feature extractors used to process the camera images and learn features for each image.']}, {'end': 2234.262, 'start': 2094.916, 'title': 'The impact of convolutional neural networks', 'summary': 'Highlights the impact of convolutional neural networks in computer vision, emphasizing its ability to learn from human driving data to autonomously navigate, its wide-reaching influence in various fields, and its role in predicting medical scans and actuating robots.', 'duration': 139.346, 'highlights': ['Convolutional neural networks demonstrate the ability to autonomously learn to navigate by observing human driving data, without explicit knowledge of lane markers, roads, or intersections.', 'Convolutional neural networks have a wide-reaching impact across various fields of computer vision, including robotics and medicine.', 'The lecture covers the origins of computer vision, the architecture of convolutional neural networks, and their applications in predicting medical scans and actuating robots in the real world.']}], 'duration': 291.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iaSUYvmCekI/pics/iaSUYvmCekI1942613.jpg', 'highlights': ['Neural networks in healthcare segment brain tumors and infected blood, improving accuracy', 'Neural networks in self-driving cars enable autonomous navigation, improving accuracy', 'Convolutional neural networks autonomously learn to navigate without explicit knowledge', 'Convolutional neural networks impact various fields of computer vision, including robotics and medicine', "Self-driving car's neural network predicts a distribution of possible control actions"]}], 'highlights': ['Deep learning revolutionizes healthcare, medicine, and autonomous vehicles with complex task capabilities and vast data (relevance: 5)', "Deep learning emphasizes vision's significance in human life, including navigation, object recognition, and emotion detection (relevance: 4)", 'Introduction of patch-based connections and weighted summation to maintain spatial structure and extract visual features (relevance: 3)', 'CNNs use convolution, pooling, and fully connected layers for feature extraction and classification (relevance: 2)', 'Neural networks in healthcare segment brain tumors and infected blood, improving accuracy (relevance: 1)']}