title
CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1

description
Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition. Lecture 2. Get in touch on Twitter @cs231n, or on Reddit /r/cs231n.

detail
{'title': 'CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1', 'heatmap': [{'end': 932.904, 'start': 894.548, 'weight': 0.866}, {'end': 1104.541, 'start': 1033.539, 'weight': 0.925}, {'end': 1312.529, 'start': 1206.761, 'weight': 0.794}, {'end': 1760.132, 'start': 1723.675, 'weight': 0.701}, {'end': 1828.012, 'start': 1791.945, 'weight': 0.739}, {'end': 2008.964, 'start': 1894.583, 'weight': 0.823}, {'end': 2487.997, 'start': 2413.388, 'weight': 0.927}, {'end': 2794.827, 'start': 2755.77, 'weight': 0.706}], 'summary': 'The lecture covers machine learning assignments due on january 20, discussing challenges in image classification, the implementation of k-nearest neighbor classifier, and the use of linear classifiers, neural networks, and convolutional neural networks to classify images into 10 classes, emphasizing the impact of weights on spatial positions in the image.', 'chapters': [{'end': 162.537, 'segs': [{'end': 93.358, 'src': 'embed', 'start': 38.539, 'weight': 0, 'content': [{'end': 42.66, 'text': 'You will be writing a k-nearest neighbor classifier, a linear classifier, and a small two-layer neural network.', 'start': 38.539, 'duration': 4.121}, {'end': 46.801, 'text': "And you'll be writing the entirety of back propagation algorithm for a two-layer neural network.", 'start': 42.8, 'duration': 4.001}, {'end': 49.322, 'text': "We'll cover all that material in the next two weeks.", 'start': 47.362, 'duration': 1.96}, {'end': 54.894, 'text': 'Warning, by the way, there are assignments from last year as well.', 'start': 52.711, 'duration': 2.183}, {'end': 56.016, 'text': "And we're changing the assignments.", 'start': 54.934, 'duration': 1.082}, {'end': 59.04, 'text': 'So they will please do not complete a 2015 assignment.', 'start': 56.156, 'duration': 2.884}, {'end': 61.183, 'text': "That's something to be aware of.", 'start': 59.741, 'duration': 1.442}, {'end': 65.248, 'text': "And for your computation, by the way, we'll be using Python and NumPy.", 'start': 61.944, 'duration': 3.304}, {'end': 68.132, 'text': "And we'll also be offering terminal.com, which is,", 'start': 66.27, 'duration': 1.862}, {'end': 73.779, 'text': "which is basically these virtual machines in the cloud that you can use if you don't have a very good laptop, and so on.", 'start': 69.614, 'duration': 4.165}, {'end': 75.321, 'text': "I'll go into detail of that in a bit.", 'start': 73.9, 'duration': 1.421}, {'end': 78.465, 'text': "I'd just like to point out that, for the first assignment,", 'start': 76.162, 'duration': 2.303}, {'end': 83.912, 'text': "we assume that you'll be relatively familiar with Python and you'll be writing these optimized NumPy expressions,", 'start': 78.465, 'duration': 5.447}, {'end': 86.976, 'text': "where you're manipulating these matrices and vectors in very efficient forms.", 'start': 83.912, 'duration': 3.064}, {'end': 93.358, 'text': "So, for example, if you're seeing this code and it doesn't mean anything to you, then please have a look at our Python NumPy tutorial.", 'start': 87.597, 'duration': 5.761}], 'summary': 'Students will cover k-nearest neighbor, linear classifier, and a two-layer neural network in the next two weeks using python and numpy.', 'duration': 54.819, 'max_score': 38.539, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE38539.jpg'}], 'start': 1.061, 'title': 'Machine learning assignments and tools', 'summary': 'Discusses upcoming machine learning assignments including k-nearest neighbor, linear classifier, and two-layer neural network, due on january 20, using python, numpy, and terminal.com for computation.', 'chapters': [{'end': 162.537, 'start': 1.061, 'title': 'Machine learning assignments and tools', 'summary': 'Discusses the upcoming assignments in machine learning, including writing a k-nearest neighbor classifier, a linear classifier, and a two-layer neural network, due on january 20, along with the use of python, numpy, and terminal.com for computation.', 'duration': 161.476, 'highlights': ['The first assignment involves writing a k-nearest neighbor classifier, a linear classifier, and a small two-layer neural network, along with the entirety of back propagation algorithm for a two-layer neural network, due on January 20.', 'The chapter emphasizes the use of Python and NumPy for computation, as well as offering terminal.com for virtual machines in the cloud, with credits being distributed to students for usage.', 'Students are advised to be familiar with Python and optimized NumPy expressions for efficient manipulation of matrices and vectors, with a tutorial available on the website for reference.']}], 'duration': 161.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1061.jpg', 'highlights': ['The first assignment involves writing a k-nearest neighbor classifier, a linear classifier, and a small two-layer neural network, along with the entirety of back propagation algorithm for a two-layer neural network, due on January 20.', 'The chapter emphasizes the use of Python and NumPy for computation, as well as offering terminal.com for virtual machines in the cloud, with credits being distributed to students for usage.', 'Students are advised to be familiar with Python and optimized NumPy expressions for efficient manipulation of matrices and vectors, with a tutorial available on the website for reference.']}, {'end': 885.821, 'segs': [{'end': 285.205, 'src': 'embed', 'start': 263.478, 'weight': 4, 'content': [{'end': 273.602, 'text': 'And so the reason that image classification is difficult is when you think about what we have to work with these millions of numbers of that form and having to classify things like cats,', 'start': 263.478, 'duration': 10.124}, {'end': 275.562, 'text': 'it quickly becomes apparent the complexity of the task.', 'start': 273.602, 'duration': 1.96}, {'end': 279.864, 'text': 'camera can be rotated around this cat.', 'start': 277.583, 'duration': 2.281}, {'end': 281.964, 'text': 'And it can be zoomed in and out and rotated, shifted.', 'start': 279.904, 'duration': 2.06}, {'end': 285.205, 'text': 'The focal properties, intrinsics of that camera can be different.', 'start': 282.564, 'duration': 2.641}], 'summary': 'Image classification is complex due to millions of variables, including camera properties and rotations.', 'duration': 21.727, 'max_score': 263.478, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE263478.jpg'}, {'end': 413.747, 'src': 'embed', 'start': 386.969, 'weight': 0, 'content': [{'end': 394.014, 'text': 'But when you consider the full cross product of all these different things And the fact that our algorithms have to work across all of that,', 'start': 386.969, 'duration': 7.045}, {'end': 395.736, 'text': "it's actually quite amazing that anything works at all.", 'start': 394.014, 'duration': 1.722}, {'end': 400.039, 'text': 'In fact, not only does it work, but it works really, really well, almost at human accuracy.', 'start': 396.696, 'duration': 3.343}, {'end': 401.86, 'text': 'We can recognize thousands of categories like this.', 'start': 400.139, 'duration': 1.721}, {'end': 405.542, 'text': 'And we can do that in a few dozen milliseconds with the current technology.', 'start': 402.14, 'duration': 3.402}, {'end': 406.963, 'text': "And so that's what you'll learn about in this class.", 'start': 405.562, 'duration': 1.401}, {'end': 413.747, 'text': "So what does an image classifier look like? Basically, we're taking this 3D array of pixel values.", 'start': 408.925, 'duration': 4.822}], 'summary': 'Algorithms work across diverse data, achieving human accuracy, recognizing thousands of categories in milliseconds.', 'duration': 26.778, 'max_score': 386.969, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE386969.jpg'}, {'end': 515.727, 'src': 'embed', 'start': 486.066, 'weight': 1, 'content': [{'end': 489.467, 'text': "It's a completely unscalable approach to classification.", 'start': 486.066, 'duration': 3.401}, {'end': 492.529, 'text': "And so the approach we're adopting in this class, and the approach that works much better,", 'start': 490.108, 'duration': 2.421}, {'end': 497.733, 'text': 'is the data-driven approach that we like in the framework of machine learning.', 'start': 493.269, 'duration': 4.464}, {'end': 503.017, 'text': 'And just to point out that in these days, actually in the early days, they did not have the luxury of using data.', 'start': 498.413, 'duration': 4.604}, {'end': 507.021, 'text': "Because at this point in time, you're taking grayscale images of very low resolution.", 'start': 503.358, 'duration': 3.663}, {'end': 509.022, 'text': "You have five images, and you're trying to recognize things.", 'start': 507.281, 'duration': 1.741}, {'end': 510.223, 'text': "It's obviously not going to work.", 'start': 509.062, 'duration': 1.161}, {'end': 515.727, 'text': 'But with the availability of internet, huge amount of data, I can search, for example, for cat on Google.', 'start': 510.804, 'duration': 4.923}], 'summary': 'Data-driven approach in machine learning outperforms unscalable methods. availability of internet data enables better recognition.', 'duration': 29.661, 'max_score': 486.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE486066.jpg'}, {'end': 621.557, 'src': 'embed', 'start': 592.856, 'weight': 2, 'content': [{'end': 598.04, 'text': "And then there's a test set of 10, 000 images where we're going to evaluate how well the classifier is working.", 'start': 592.856, 'duration': 5.184}, {'end': 599.661, 'text': 'And these images are quite tiny.', 'start': 598.581, 'duration': 1.08}, {'end': 603.084, 'text': "They're just a little toy dataset of 32 by 32 little thumbnail images.", 'start': 599.681, 'duration': 3.403}, {'end': 608.588, 'text': "So the way nearest neighbor classifier would work is we take all this training data that's given to us, 50, 000 images.", 'start': 604.125, 'duration': 4.463}, {'end': 612.731, 'text': 'Now at test time, suppose we have these 10 different examples here.', 'start': 609.168, 'duration': 3.563}, {'end': 614.672, 'text': 'These are test images along the first column here.', 'start': 612.851, 'duration': 1.821}, {'end': 621.557, 'text': "What we'll do is we'll look up nearest neighbors in the training set of things that are most similar to every one of those images independently.", 'start': 615.112, 'duration': 6.445}], 'summary': 'Evaluation of classifier using 10,000 tiny test images and 50,000 training images.', 'duration': 28.701, 'max_score': 592.856, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE592856.jpg'}, {'end': 850.829, 'src': 'embed', 'start': 818.499, 'weight': 3, 'content': [{'end': 820.579, 'text': "We'll see that we do a huge amount of compute at train time.", 'start': 818.499, 'duration': 2.08}, {'end': 822.38, 'text': "We'll be training a convolutional neural network.", 'start': 820.859, 'duration': 1.521}, {'end': 824.68, 'text': 'But the test time performance will be super efficient.', 'start': 822.82, 'duration': 1.86}, {'end': 826.801, 'text': 'In fact, it will be constant amount of compute.', 'start': 824.96, 'duration': 1.841}, {'end': 830.342, 'text': "For every single test image, we'll do constant amount of computation.", 'start': 827.841, 'duration': 2.501}, {'end': 837.684, 'text': "No matter if you have a million, billion, or trillion training images, I'd like to have a trillion training images.", 'start': 830.702, 'duration': 6.982}, {'end': 843.126, 'text': "No matter how large your training data set is, we'll do a constant compute to classify any single testing example.", 'start': 838.144, 'duration': 4.982}, {'end': 845.287, 'text': "So that's very nice, practically speaking.", 'start': 843.346, 'duration': 1.941}, {'end': 850.829, 'text': "Now, I'll just like to point out that there are ways of speeding up nearest neighbor classifiers.", 'start': 846.047, 'duration': 4.782}], 'summary': 'Efficient test time performance with constant computation for any size of training dataset.', 'duration': 32.33, 'max_score': 818.499, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE818499.jpg'}], 'start': 165.62, 'title': 'Image classification challenges and nearest neighbor classifier', 'summary': 'Discusses challenges in image classification such as semantic gap, illumination, deformation, and more, emphasizing the success of current technology. it also covers the implementation of nearest neighbor classifier, emphasizing its implications on speed and the trade-off between computational load at train time and test time.', 'chapters': [{'end': 647.448, 'start': 165.62, 'title': 'Image classification basics', 'summary': 'Discusses the challenges of image classification, including the semantic gap, illumination, deformation, occlusion, background clutter, and intraclass variation, emphasizing the complexity of the task and the success of current technology in achieving human-level accuracy in recognizing thousands of categories in just a few milliseconds.', 'duration': 481.828, 'highlights': ['The complexity of image classification is attributed to various challenges such as the semantic gap, illumination, deformation, occlusion, background clutter, and intraclass variation, highlighting the difficulty of robustly recognizing objects in images.', 'Current technology has achieved the capability to recognize thousands of categories in images with human-level accuracy and process this recognition in just a few dozen milliseconds, demonstrating the remarkable success of image classification algorithms.', "The data-driven approach in machine learning, facilitated by the availability of a large amount of internet data, allows for training models to classify new test data, exemplified by the nearest neighbor classifier's utilization of a training set of 50,000 images to classify 10,000 test images in the CIFAR-10 dataset.", 'The nearest neighbor classifier works by comparing test images to every single image in the training set and transferring the label over based on the most similar images, demonstrating the practical application of training models to classify new data in image recognition tasks.']}, {'end': 885.821, 'start': 648.008, 'title': 'Nearest neighbor classifier', 'summary': "Discusses the implementation of a nearest neighbor classifier using manhattan distance and its implications on speed, emphasizing its linear slowdown with increased training data and the trade-off between computational load at train time and test time, contrasting it with the efficiency of comnet's test time performance.", 'duration': 237.813, 'highlights': ["The nearest neighbor classifier's speed is linearly slower with increased training data, leading to a trade-off between computational load at train time and test time. The speed of the nearest neighbor classifier is linearly slower with increased training data, impacting the computational load at train time and test time, highlighting the trade-off between efficiency during training and testing.", "ComNet's test time performance is super efficient, requiring a constant amount of compute regardless of the size of the training data set. ComNet's test time performance is super efficient, necessitating a constant amount of compute for classifying any single testing example, irrespective of the size of the training data set.", 'The Manhattan distance is used to compare the absolute value differences of pixel positions in the nearest neighbor classifier. The Manhattan distance is utilized to compare the absolute value differences of pixel positions in the nearest neighbor classifier, providing insights into the comparison process.']}], 'duration': 720.201, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE165620.jpg', 'highlights': ['Current technology achieves human-level accuracy in recognizing thousands of image categories in milliseconds.', 'The data-driven approach in machine learning allows training models to classify new test data.', 'The nearest neighbor classifier compares test images to every image in the training set for practical application in image recognition tasks.', "ComNet's test time performance is super efficient, requiring a constant amount of compute regardless of the training data size.", 'The semantic gap, illumination, deformation, and other challenges contribute to the complexity of image classification.']}, {'end': 1503.304, 'segs': [{'end': 932.904, 'src': 'heatmap', 'start': 894.548, 'weight': 0.866, 'content': [{'end': 903.347, 'text': 'What happened there? Did someone push a button over there in the back? OK, thank you.', 'start': 894.548, 'duration': 8.799}, {'end': 909.33, 'text': "So this choice of how exactly we compute a distance, it's a discrete choice that we have control over.", 'start': 904.348, 'duration': 4.982}, {'end': 911.131, 'text': "That's something we call the hyperparameter.", 'start': 909.83, 'duration': 1.301}, {'end': 912.591, 'text': "It's not really obvious how you set it.", 'start': 911.271, 'duration': 1.32}, {'end': 913.812, 'text': "It's a hyperparameter.", 'start': 913.012, 'duration': 0.8}, {'end': 916.433, 'text': 'We have to decide later on exactly how to set this somehow.', 'start': 913.952, 'duration': 2.481}, {'end': 924.337, 'text': "Another sort of hyperparameter that I'll talk about in context of nearest neighbor classifier is when we generalize nearest neighbor to what we call a k-nearest neighbor classifier.", 'start': 917.494, 'duration': 6.843}, {'end': 930.241, 'text': 'So, in a k-nearest neighbor classifier, instead of retrieving for every test image the single nearest training example,', 'start': 925.197, 'duration': 5.044}, {'end': 932.904, 'text': "we'll in fact retrieve several nearest examples.", 'start': 930.241, 'duration': 2.663}], 'summary': 'Discussion on hyperparameters and k-nearest neighbor classifier in machine learning.', 'duration': 38.356, 'max_score': 894.548, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE894548.jpg'}, {'end': 1001.067, 'src': 'embed', 'start': 976.514, 'weight': 0, 'content': [{'end': 983.397, 'text': 'And it has its own little region of influence where it would have classified a lot of test points around it as green, because if any point fell there,', 'start': 976.514, 'duration': 6.883}, {'end': 985.138, 'text': 'then that green point would have been the nearest neighbor.', 'start': 983.397, 'duration': 1.741}, {'end': 991.661, 'text': 'Now, when you move to higher numbers for k, such as 5 nearest neighbor classifier, what you find is that the boundaries start to smooth out.', 'start': 985.918, 'duration': 5.743}, {'end': 993.222, 'text': "It's kind of this nice.", 'start': 992.121, 'duration': 1.101}, {'end': 1001.067, 'text': 'effect where, even if there is this one point kind of randomly as a noise, an outlier in the blue cluster,', 'start': 994.242, 'duration': 6.825}], 'summary': 'Knn classifier smooths boundaries with higher k values.', 'duration': 24.553, 'max_score': 976.514, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE976514.jpg'}, {'end': 1104.541, 'src': 'heatmap', 'start': 1033.539, 'weight': 0.925, 'content': [{'end': 1037.584, 'text': "OK So let's do a bit of questions here, just for fun.", 'start': 1033.539, 'duration': 4.045}, {'end': 1044.75, 'text': "Consider what is the accuracy of the nearest neighbor classifier on the training data when we're using Euclidean distance?", 'start': 1038.704, 'duration': 6.046}, {'end': 1050.835, 'text': "So suppose our test set is exactly the training data and we're trying to find the accuracy.", 'start': 1045.05, 'duration': 5.785}, {'end': 1053.277, 'text': 'In other words, how often would we get the correct answer? 100%.', 'start': 1050.935, 'duration': 2.342}, {'end': 1055.379, 'text': '100% Good.', 'start': 1053.277, 'duration': 2.102}, {'end': 1063.283, 'text': "Why? OK, among the murmurs, yeah, that's correct.", 'start': 1055.399, 'duration': 7.884}, {'end': 1069.608, 'text': 'So we always find a training example exactly on top of that test, which has zero distance, and then it will be transferred over.', 'start': 1063.703, 'duration': 5.905}, {'end': 1083.498, 'text': "Good What if we're using the Manhattan distance instead? So the Manhattan distance doesn't use sum of squares.", 'start': 1069.848, 'duration': 13.65}, {'end': 1085.22, 'text': 'It uses sum of absolute values of differences.', 'start': 1083.558, 'duration': 1.662}, {'end': 1087.542, 'text': "Wouldn't it be the same? It would.", 'start': 1085.24, 'duration': 2.302}, {'end': 1088.462, 'text': "It's just up to your question.", 'start': 1087.642, 'duration': 0.82}, {'end': 1089.143, 'text': 'It would be the same.', 'start': 1088.642, 'duration': 0.501}, {'end': 1092.088, 'text': 'OK, good.', 'start': 1091.488, 'duration': 0.6}, {'end': 1095.45, 'text': "So we're paying attention here.", 'start': 1092.889, 'duration': 2.561}, {'end': 1099.412, 'text': 'What is the accuracy of the k nearest neighbor classifier in the training data? Say k was 5.', 'start': 1096.431, 'duration': 2.981}, {'end': 1104.541, 'text': 'Is it 100%? Not necessarily, right? Good.', 'start': 1099.412, 'duration': 5.129}], 'summary': 'Nearest neighbor classifier accuracy on training data using euclidean and manhattan distance, k=5 not 100%', 'duration': 71.002, 'max_score': 1033.539, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1033539.jpg'}, {'end': 1063.283, 'src': 'embed', 'start': 1038.704, 'weight': 3, 'content': [{'end': 1044.75, 'text': "Consider what is the accuracy of the nearest neighbor classifier on the training data when we're using Euclidean distance?", 'start': 1038.704, 'duration': 6.046}, {'end': 1050.835, 'text': "So suppose our test set is exactly the training data and we're trying to find the accuracy.", 'start': 1045.05, 'duration': 5.785}, {'end': 1053.277, 'text': 'In other words, how often would we get the correct answer? 100%.', 'start': 1050.935, 'duration': 2.342}, {'end': 1055.379, 'text': '100% Good.', 'start': 1053.277, 'duration': 2.102}, {'end': 1063.283, 'text': "Why? OK, among the murmurs, yeah, that's correct.", 'start': 1055.399, 'duration': 7.884}], 'summary': 'Nearest neighbor classifier achieves 100% accuracy using euclidean distance on training data.', 'duration': 24.579, 'max_score': 1038.704, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1038704.jpg'}, {'end': 1312.529, 'src': 'heatmap', 'start': 1206.761, 'weight': 0.794, 'content': [{'end': 1209.063, 'text': 'And a lot of this, by the way, 229 is a requirement for this class.', 'start': 1206.761, 'duration': 2.302}, {'end': 1211.005, 'text': 'So you should be quite familiar with this.', 'start': 1209.444, 'duration': 1.561}, {'end': 1214.448, 'text': 'This is to most extent, this is kind of more of a review for you.', 'start': 1211.045, 'duration': 3.403}, {'end': 1218.63, 'text': 'But basically, this test data is used very sparingly.', 'start': 1215.569, 'duration': 3.061}, {'end': 1219.551, 'text': 'Forget that you have it.', 'start': 1218.81, 'duration': 0.741}, {'end': 1223.112, 'text': 'Instead, what we do is we separate our training data into what we call folds.', 'start': 1219.891, 'duration': 3.221}, {'end': 1227.374, 'text': 'So we use a five-fold validation.', 'start': 1223.712, 'duration': 3.662}, {'end': 1231.735, 'text': 'So we use 20% of the training data as an imagined test set data.', 'start': 1227.894, 'duration': 3.841}, {'end': 1233.476, 'text': 'And then we only train on part of it.', 'start': 1232.135, 'duration': 1.341}, {'end': 1238.098, 'text': 'And we test out the choices of hyperparameters on this validation set.', 'start': 1233.736, 'duration': 4.362}, {'end': 1243.543, 'text': "So I'm going to train on my fourfolds and try out all the different case and all the different distance metrics and whatever else.", 'start': 1238.698, 'duration': 4.845}, {'end': 1246.065, 'text': "If you're using approximate nearest neighbor, you have many other choices.", 'start': 1243.663, 'duration': 2.402}, {'end': 1248.748, 'text': 'You try it out, see what works best on that validation data.', 'start': 1246.506, 'duration': 2.242}, {'end': 1252.171, 'text': "If you're feeling uncomfortable because you have very few training data points.", 'start': 1249.168, 'duration': 3.003}, {'end': 1257.336, 'text': 'people also sometimes use cross-validation, where you actually iterate the choice of your test or validation.', 'start': 1252.171, 'duration': 5.165}, {'end': 1258.798, 'text': 'fold across these choices.', 'start': 1257.336, 'duration': 1.462}, {'end': 1263.541, 'text': "So I'll first use 1 to 4 for my training and try out on 5.", 'start': 1259.358, 'duration': 4.183}, {'end': 1266.764, 'text': 'And then I cycle the choice of the validation fold across all the five choices.', 'start': 1263.541, 'duration': 3.223}, {'end': 1270.987, 'text': 'And I look at what works best across all the possible choices of my test fold.', 'start': 1267.304, 'duration': 3.683}, {'end': 1274.41, 'text': 'And then I just take whatever works best across all the possible scenarios.', 'start': 1271.608, 'duration': 2.802}, {'end': 1277.913, 'text': "That's referred to as a cross-validation set.", 'start': 1275.251, 'duration': 2.662}, {'end': 1282.296, 'text': "So in practice, the way this would look like, say we're cross-validating for k for a nearest neighbor classifier.", 'start': 1278.453, 'duration': 3.843}, {'end': 1285.478, 'text': 'is we are trying out different values of k.', 'start': 1283.277, 'duration': 2.201}, {'end': 1290.02, 'text': 'And this is our performance across five choices of the fold.', 'start': 1285.478, 'duration': 4.542}, {'end': 1293.641, 'text': 'So you can see that for every single k, we have five data points there.', 'start': 1290.58, 'duration': 3.061}, {'end': 1295.482, 'text': 'And then this is the accuracy.', 'start': 1294.281, 'duration': 1.201}, {'end': 1296.242, 'text': 'So high is good.', 'start': 1295.602, 'duration': 0.64}, {'end': 1298.543, 'text': "And I'm plotting a line through the mean.", 'start': 1296.682, 'duration': 1.861}, {'end': 1300.984, 'text': "And I'm also showing the bars for the standard deviations.", 'start': 1298.643, 'duration': 2.341}, {'end': 1303.345, 'text': 'So what we see here is that the performance goes up.', 'start': 1301.424, 'duration': 1.921}, {'end': 1307.106, 'text': 'on the across these validation folds as you go up.', 'start': 1303.965, 'duration': 3.141}, {'end': 1308.447, 'text': 'But at some point, it starts to decay.', 'start': 1307.226, 'duration': 1.221}, {'end': 1312.529, 'text': 'So for this particular data set, it seems that k equal to 7 is the best choice.', 'start': 1308.827, 'duration': 3.702}], 'summary': 'Using five-fold validation, k=7 is best for this dataset.', 'duration': 105.768, 'max_score': 1206.761, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1206761.jpg'}, {'end': 1238.098, 'src': 'embed', 'start': 1211.045, 'weight': 2, 'content': [{'end': 1214.448, 'text': 'This is to most extent, this is kind of more of a review for you.', 'start': 1211.045, 'duration': 3.403}, {'end': 1218.63, 'text': 'But basically, this test data is used very sparingly.', 'start': 1215.569, 'duration': 3.061}, {'end': 1219.551, 'text': 'Forget that you have it.', 'start': 1218.81, 'duration': 0.741}, {'end': 1223.112, 'text': 'Instead, what we do is we separate our training data into what we call folds.', 'start': 1219.891, 'duration': 3.221}, {'end': 1227.374, 'text': 'So we use a five-fold validation.', 'start': 1223.712, 'duration': 3.662}, {'end': 1231.735, 'text': 'So we use 20% of the training data as an imagined test set data.', 'start': 1227.894, 'duration': 3.841}, {'end': 1233.476, 'text': 'And then we only train on part of it.', 'start': 1232.135, 'duration': 1.341}, {'end': 1238.098, 'text': 'And we test out the choices of hyperparameters on this validation set.', 'start': 1233.736, 'duration': 4.362}], 'summary': 'Five-fold validation uses 20% of training data as test set for hyperparameter testing.', 'duration': 27.053, 'max_score': 1211.045, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1211045.jpg'}, {'end': 1439.802, 'src': 'embed', 'start': 1413.558, 'weight': 1, 'content': [{'end': 1418.002, 'text': 'And different data sets will require different choices, and you need to see what works best.', 'start': 1413.558, 'duration': 4.444}, {'end': 1422.986, 'text': "In fact, when you try out different algorithms because you're not sure what's going to work best on your data,", 'start': 1418.602, 'duration': 4.384}, {'end': 1425.268, 'text': 'the choice of your algorithm is also kind of like a hyperparameter.', 'start': 1422.986, 'duration': 2.282}, {'end': 1427.37, 'text': "So you're just not sure what works.", 'start': 1425.929, 'duration': 1.441}, {'end': 1428.731, 'text': "You're not different.", 'start': 1427.61, 'duration': 1.121}, {'end': 1432.979, 'text': 'approaches will give you different generalization boundaries.', 'start': 1430.638, 'duration': 2.341}, {'end': 1434.24, 'text': 'They will look different.', 'start': 1433.519, 'duration': 0.721}, {'end': 1436.741, 'text': 'And some data sets have different structure than others.', 'start': 1434.34, 'duration': 2.401}, {'end': 1438.081, 'text': 'So some things work better than others.', 'start': 1436.921, 'duration': 1.16}, {'end': 1439.802, 'text': 'You have to just try it out.', 'start': 1438.641, 'duration': 1.161}], 'summary': 'Choosing algorithms for different data sets impacts generalization boundaries and performance.', 'duration': 26.244, 'max_score': 1413.558, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1413558.jpg'}], 'start': 885.821, 'title': 'K-nearest neighbor classifier', 'summary': 'Discusses the concept of hyperparameters and introduces the k-nearest neighbor classifier, emphasizing the impact of k on classification boundaries and the superior performance at test time. it also highlights the challenges of setting hyperparameters, the importance of cross-validation, and the limitations of using distance metrics on high-dimensional data.', 'chapters': [{'end': 1092.088, 'start': 885.821, 'title': 'K-nearest neighbor classifier', 'summary': 'Discusses the concept of hyperparameters and introduces the k-nearest neighbor classifier, highlighting the impact of k on classification boundaries and the superior performance of k-nearest neighbor classifiers at test time.', 'duration': 206.267, 'highlights': ['The k-nearest neighbor classifier retrieves several nearest examples and performs a majority vote over the classes to classify every test instance, with a 5-nearest neighbor yielding a majority vote of the labels from the five most similar images in the training data. The k-nearest neighbor classifier retrieves several nearest examples and performs a majority vote over the classes to classify every test instance, with a 5-nearest neighbor yielding a majority vote of the labels from the five most similar images in the training data.', 'The boundaries of the nearest neighbor classifier start to smooth out as the k value increases, resulting in better performance at test time due to the overwhelming influence of multiple nearest neighbors, making k a crucial hyperparameter for the classifier. The boundaries of the nearest neighbor classifier start to smooth out as the k value increases, resulting in better performance at test time due to the overwhelming influence of multiple nearest neighbors, making k a crucial hyperparameter for the classifier.', 'The accuracy of the nearest neighbor classifier on the training data is 100% when using Euclidean distance, as the test set is exactly the training data, resulting in the correct answer every time. The accuracy of the nearest neighbor classifier on the training data is 100% when using Euclidean distance, as the test set is exactly the training data, resulting in the correct answer every time.', "The choice of k in k-nearest neighbor classifiers is a hyperparameter and plays a significant role in the classifier's performance at test time. The choice of k in k-nearest neighbor classifiers is a hyperparameter and plays a significant role in the classifier's performance at test time."]}, {'end': 1503.304, 'start': 1092.889, 'title': 'K-nearest neighbor classifier', 'summary': 'Discusses the challenges of setting hyperparameters for the k-nearest neighbor classifier, emphasizing the importance of cross-validation to avoid overfitting and the inefficiency and limitations of using distance metrics on high-dimensional data.', 'duration': 410.415, 'highlights': ['Cross-validation is essential for setting hyperparameters and ensuring generalization, as different hyperparameters may work better for different applications. The chapter emphasizes the need for cross-validation to determine the best hyperparameters, highlighting that different choices may work better for different applications.', 'The inefficiency and limitations of using the k-nearest neighbor classifier due to its inefficiency and the unnatural behavior of distance metrics on high-dimensional objects, particularly images. The inefficiency and limitations of using the k-nearest neighbor classifier, especially on high-dimensional objects like images, due to the unnatural behavior of distance metrics, are discussed.', 'The importance of setting aside test data and using it sparingly, and the utilization of folds and validation sets for training and testing hyperparameter choices. The chapter stresses the importance of setting aside test data, using it sparingly, and utilizing folds and validation sets for training and testing hyperparameter choices.']}], 'duration': 617.483, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE885821.jpg', 'highlights': ['The boundaries of the nearest neighbor classifier start to smooth out as the k value increases, resulting in better performance at test time due to the overwhelming influence of multiple nearest neighbors, making k a crucial hyperparameter for the classifier.', 'Cross-validation is essential for setting hyperparameters and ensuring generalization, as different hyperparameters may work better for different applications.', 'The importance of setting aside test data and using it sparingly, and the utilization of folds and validation sets for training and testing hyperparameter choices.', 'The accuracy of the nearest neighbor classifier on the training data is 100% when using Euclidean distance, as the test set is exactly the training data, resulting in the correct answer every time.']}, {'end': 1803.237, 'segs': [{'end': 1556.705, 'src': 'embed', 'start': 1503.304, 'weight': 0, 'content': [{'end': 1508.427, 'text': 'and the nearest neighbor classifier would not be able to really tell the difference between these settings,', 'start': 1503.304, 'duration': 5.123}, {'end': 1511.41, 'text': "because it's based on these distances that don't really work very well in this case.", 'start': 1508.427, 'duration': 2.983}, {'end': 1516.88, 'text': 'So very unintuitive things happen when you try to throw distances on very high dimensional objects.', 'start': 1512.21, 'duration': 4.67}, {'end': 1518.182, 'text': "That's partly why we don't use this.", 'start': 1517, 'duration': 1.182}, {'end': 1524.962, 'text': "So in summary so far, we're looking at image classification as a specific case.", 'start': 1520.339, 'duration': 4.623}, {'end': 1527.344, 'text': "And we'll go into different settings later in the class.", 'start': 1525.322, 'duration': 2.022}, {'end': 1531.046, 'text': "I've introduced the nearest-nameable classifier and the idea of having different splits of your data.", 'start': 1527.804, 'duration': 3.242}, {'end': 1534.328, 'text': 'And we have these hyperparameters that we need to pick.', 'start': 1531.786, 'duration': 2.542}, {'end': 1536.47, 'text': 'And we use cross-validation for this usually.', 'start': 1534.768, 'duration': 1.702}, {'end': 1539.171, 'text': "Most of the time, people don't actually use entire cross-validation.", 'start': 1536.91, 'duration': 2.261}, {'end': 1540.692, 'text': 'They just have a single validation set.', 'start': 1539.211, 'duration': 1.481}, {'end': 1544.235, 'text': 'And they try out on the validation set whatever works best in terms of the hyperparameters.', 'start': 1541.053, 'duration': 3.182}, {'end': 1548.699, 'text': 'And once you get the best hat parameters, you evaluate a single time on a test set.', 'start': 1545.356, 'duration': 3.343}, {'end': 1553.062, 'text': "So I'm going to go into linear classification in a bit.", 'start': 1550.42, 'duration': 2.642}, {'end': 1556.705, 'text': 'Any questions at this point? Otherwise? Great.', 'start': 1553.202, 'duration': 3.503}], 'summary': 'Nearest neighbor classifier struggles in high dimensions. cross-validation used for hyperparameter tuning.', 'duration': 53.401, 'max_score': 1503.304, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1503304.jpg'}, {'end': 1607.336, 'src': 'embed', 'start': 1578.679, 'weight': 4, 'content': [{'end': 1580.201, 'text': 'So this class is a computer vision class.', 'start': 1578.679, 'duration': 1.522}, {'end': 1583.423, 'text': "We're interested in giving machines sight.", 'start': 1580.421, 'duration': 3.002}, {'end': 1589.448, 'text': "Another way to motivate this class would be from a model-based point of view, in the sense that we're giving you guys,", 'start': 1584.184, 'duration': 5.264}, {'end': 1592.53, 'text': "We're teaching guys about deep learning and neural networks.", 'start': 1590.649, 'duration': 1.881}, {'end': 1596.791, 'text': 'These are wonderful algorithms that you can apply to many different data domains, not just vision.', 'start': 1592.83, 'duration': 3.961}, {'end': 1598.832, 'text': 'So, in particular, over the last few years,', 'start': 1597.452, 'duration': 1.38}, {'end': 1604.975, 'text': "we saw that neural networks can not only see that's what you'll learn a lot about in this class but they can also hear.", 'start': 1598.832, 'duration': 6.143}, {'end': 1607.336, 'text': "They're used quite a bit in speech recognition now.", 'start': 1605.215, 'duration': 2.121}], 'summary': 'Computer vision class teaches deep learning and neural networks with applications beyond vision.', 'duration': 28.657, 'max_score': 1578.679, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1578679.jpg'}, {'end': 1695.013, 'src': 'embed', 'start': 1666.064, 'weight': 5, 'content': [{'end': 1670.428, 'text': "And she's building what looks to be a roughly 10 layer convolutional neural network at this point.", 'start': 1666.064, 'duration': 4.364}, {'end': 1672.529, 'text': 'And so these are very fun.', 'start': 1671.729, 'duration': 0.8}, {'end': 1676.233, 'text': 'Really the best way to think about playing with neural networks is like LEGO blocks.', 'start': 1672.91, 'duration': 3.323}, {'end': 1681.918, 'text': "You'll see that we're building these little function pieces, these LEGO blocks that we can stack together to create entire architectures.", 'start': 1676.493, 'duration': 5.425}, {'end': 1683.799, 'text': 'And they very easily talk to each other.', 'start': 1682.298, 'duration': 1.501}, {'end': 1688.543, 'text': 'And so we can just create these modules and stack them together and play with this very easily.', 'start': 1684.42, 'duration': 4.123}, {'end': 1695.013, 'text': 'One work that I think exemplifies this is my own work on image captioning from roughly a year ago.', 'start': 1690.189, 'duration': 4.824}], 'summary': 'Building a 10-layer convolutional neural network, likened to lego blocks, for image captioning.', 'duration': 28.949, 'max_score': 1666.064, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1666064.jpg'}, {'end': 1752.709, 'src': 'embed', 'start': 1723.675, 'weight': 6, 'content': [{'end': 1728.078, 'text': "So there's two modules here in this system diagram for image captioning model.", 'start': 1723.675, 'duration': 4.403}, {'end': 1730.759, 'text': "We're taking a convolutional neural network, which we know can see.", 'start': 1728.478, 'duration': 2.281}, {'end': 1734.982, 'text': "And we're taking a recurrent neural network, which we know is very good at modeling sequences.", 'start': 1730.779, 'duration': 4.203}, {'end': 1737.844, 'text': 'In this case, sequences of words that will be describing the image.', 'start': 1735.142, 'duration': 2.702}, {'end': 1741.925, 'text': 'And then just as if we were playing with LEGOs, we take those two pieces and we stick them together.', 'start': 1738.504, 'duration': 3.421}, {'end': 1744.786, 'text': "That's corresponding to this arrow here in between the two modules.", 'start': 1742.225, 'duration': 2.561}, {'end': 1747.287, 'text': 'And these networks learn to talk to each other.', 'start': 1745.587, 'duration': 1.7}, {'end': 1752.709, 'text': 'And in the process of trying to describe the images, these gradients will be flowing through the convolutional network.', 'start': 1747.867, 'duration': 4.842}], 'summary': 'Image captioning model combines cnn and rnn to describe images using sequences of words.', 'duration': 29.034, 'max_score': 1723.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1723675.jpg'}, {'end': 1760.132, 'src': 'heatmap', 'start': 1723.675, 'weight': 0.701, 'content': [{'end': 1728.078, 'text': "So there's two modules here in this system diagram for image captioning model.", 'start': 1723.675, 'duration': 4.403}, {'end': 1730.759, 'text': "We're taking a convolutional neural network, which we know can see.", 'start': 1728.478, 'duration': 2.281}, {'end': 1734.982, 'text': "And we're taking a recurrent neural network, which we know is very good at modeling sequences.", 'start': 1730.779, 'duration': 4.203}, {'end': 1737.844, 'text': 'In this case, sequences of words that will be describing the image.', 'start': 1735.142, 'duration': 2.702}, {'end': 1741.925, 'text': 'And then just as if we were playing with LEGOs, we take those two pieces and we stick them together.', 'start': 1738.504, 'duration': 3.421}, {'end': 1744.786, 'text': "That's corresponding to this arrow here in between the two modules.", 'start': 1742.225, 'duration': 2.561}, {'end': 1747.287, 'text': 'And these networks learn to talk to each other.', 'start': 1745.587, 'duration': 1.7}, {'end': 1752.709, 'text': 'And in the process of trying to describe the images, these gradients will be flowing through the convolutional network.', 'start': 1747.867, 'duration': 4.842}, {'end': 1757.431, 'text': 'And the full system will be adjusting itself to better see the images in order to describe them at the end.', 'start': 1752.749, 'duration': 4.682}, {'end': 1760.132, 'text': 'And so this whole system will work together as one.', 'start': 1757.971, 'duration': 2.161}], 'summary': 'Image captioning model combines cnn and rnn to describe images, adjusting to better see images.', 'duration': 36.457, 'max_score': 1723.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1723675.jpg'}, {'end': 1815.604, 'src': 'embed', 'start': 1787.4, 'weight': 7, 'content': [{'end': 1791.164, 'text': "And the way we're going to approach linear classification is from what we call a parametric approach.", 'start': 1787.4, 'duration': 3.764}, {'end': 1796.029, 'text': 'K-nearest neighbor that we just discussed now is something, an instance of what we call non-parametric approach.', 'start': 1791.945, 'duration': 4.084}, {'end': 1798.152, 'text': "There's no parameters that we're going to be optimizing over.", 'start': 1796.149, 'duration': 2.003}, {'end': 1801.195, 'text': 'This distinction will become clear in a few minutes.', 'start': 1798.532, 'duration': 2.663}, {'end': 1803.237, 'text': 'So, in the parametric approach,', 'start': 1802.356, 'duration': 0.881}, {'end': 1810.481, 'text': "what we're doing is we're thinking about constructing a function that takes an image and produces the scores for your classes.", 'start': 1803.237, 'duration': 7.244}, {'end': 1811.302, 'text': 'This is what we want to do.', 'start': 1810.501, 'duration': 0.801}, {'end': 1815.604, 'text': "We want to take an image, and we'd like to figure out which one of the 10 classes it is.", 'start': 1811.622, 'duration': 3.982}], 'summary': 'Linear classification: parametric approach for optimizing parameters and producing class scores.', 'duration': 28.204, 'max_score': 1787.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1787400.jpg'}], 'start': 1503.304, 'title': 'Image classification and neural networks', 'summary': 'Discusses challenges in high-dimensional object distance for image classification, hyperparameter selection, cross-validation, and the use of neural networks in various domains. additionally, it covers linear classification using a parametric approach on the cifar-10 dataset.', 'chapters': [{'end': 1556.705, 'start': 1503.304, 'title': 'Image classification and nearest neighbor classifier', 'summary': 'Discusses the challenges of using distances in high dimensional objects for image classification, the need for picking hyperparameters, and the use of cross-validation. it also emphasizes the common practice of using a single validation set for hyperparameter selection and evaluation on a test set.', 'duration': 53.401, 'highlights': ['The challenges of using distances in high dimensional objects for image classification are discussed, highlighting the limitations of the nearest neighbor classifier in distinguishing between settings.', 'The importance of picking hyperparameters and the use of cross-validation for this purpose is emphasized, with a mention of the common practice of using a single validation set for hyperparameter selection.', 'The process of evaluating the best hyperparameters on a test set is mentioned as a common practice in image classification.', 'The introduction of the nearest-neighbor classifier and the concept of different data splits are highlighted as key points in the discussion.']}, {'end': 1803.237, 'start': 1559.647, 'title': 'Linear classification & neural networks', 'summary': 'Covers the motivation for the computer vision class, the versatility of neural networks in various domains including speech recognition and machine translation, the modular nature of neural networks as lego blocks, and the image captioning model involving convolutional and recurrent neural networks. the class will also delve into linear classification using a parametric approach on the cifar-10 dataset.', 'duration': 243.59, 'highlights': ['The chapter covers the motivation for the computer vision class, the versatility of neural networks in various domains including speech recognition and machine translation. The chapter emphasizes the motivation for the computer vision class and highlights the versatility of neural networks in domains like speech recognition and machine translation.', 'The modular nature of neural networks is described as LEGO blocks, allowing easy creation of architectures. The modular nature of neural networks is likened to LEGO blocks, enabling the easy creation of architectures for various tasks.', 'The image captioning model is explained, involving the use of convolutional and recurrent neural networks, and the flow of gradients to better describe images. The image captioning model is detailed, involving the use of convolutional and recurrent neural networks, with the gradients flowing through the convolutional network to improve image understanding.', 'The class will also delve into linear classification using a parametric approach on the CIFAR-10 dataset. The class will cover linear classification using a parametric approach on the CIFAR-10 dataset, distinguishing it from the non-parametric approach of K-nearest neighbor.']}], 'duration': 299.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1503304.jpg', 'highlights': ['The challenges of using distances in high dimensional objects for image classification are discussed, highlighting the limitations of the nearest neighbor classifier in distinguishing between settings.', 'The importance of picking hyperparameters and the use of cross-validation for this purpose is emphasized, with a mention of the common practice of using a single validation set for hyperparameter selection.', 'The process of evaluating the best hyperparameters on a test set is mentioned as a common practice in image classification.', 'The introduction of the nearest-neighbor classifier and the concept of different data splits are highlighted as key points in the discussion.', 'The chapter emphasizes the motivation for the computer vision class and highlights the versatility of neural networks in domains like speech recognition and machine translation.', 'The modular nature of neural networks is likened to LEGO blocks, enabling the easy creation of architectures for various tasks.', 'The image captioning model is detailed, involving the use of convolutional and recurrent neural networks, with the gradients flowing through the convolutional network to improve image understanding.', 'The class will cover linear classification using a parametric approach on the CIFAR-10 dataset, distinguishing it from the non-parametric approach of K-nearest neighbor.']}, {'end': 2303.948, 'segs': [{'end': 1840.762, 'src': 'embed', 'start': 1803.237, 'weight': 0, 'content': [{'end': 1810.481, 'text': "what we're doing is we're thinking about constructing a function that takes an image and produces the scores for your classes.", 'start': 1803.237, 'duration': 7.244}, {'end': 1811.302, 'text': 'This is what we want to do.', 'start': 1810.501, 'duration': 0.801}, {'end': 1815.604, 'text': "We want to take an image, and we'd like to figure out which one of the 10 classes it is.", 'start': 1811.622, 'duration': 3.982}, {'end': 1821.488, 'text': "So we'd like to write down a function, an expression, that takes an image and gives you those 10 numbers.", 'start': 1816.165, 'duration': 5.323}, {'end': 1828.012, 'text': "But the expression is not only a function of that image, but critically it will be also a function of these parameters that I'll call w,", 'start': 1822.008, 'duration': 6.004}, {'end': 1829.073, 'text': 'sometimes also called the weights.', 'start': 1828.012, 'duration': 1.061}, {'end': 1835.598, 'text': "And so really, it's a function that goes from 3, 072 numbers, which make up this image, to 10 numbers.", 'start': 1829.753, 'duration': 5.845}, {'end': 1836.599, 'text': "That's what we're doing.", 'start': 1836.098, 'duration': 0.501}, {'end': 1837.6, 'text': "We're defining a function.", 'start': 1836.639, 'duration': 0.961}, {'end': 1840.762, 'text': "And we'll go through several choices of this function.", 'start': 1838.961, 'duration': 1.801}], 'summary': 'Constructing a function to produce scores for 10 classes from an image using parameters and weights.', 'duration': 37.525, 'max_score': 1803.237, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1803237.jpg'}, {'end': 2008.964, 'src': 'heatmap', 'start': 1894.583, 'weight': 0.823, 'content': [{'end': 1899.044, 'text': "and I'm stretching out all the pixels in that image into a giant column vector.", 'start': 1894.583, 'duration': 4.461}, {'end': 1902.665, 'text': 'So that x there is a column vector of 3, 072 numbers.', 'start': 1899.524, 'duration': 3.141}, {'end': 1910.944, 'text': 'And so if you know your matrix vector operations, which you should.', 'start': 1905.185, 'duration': 5.759}, {'end': 1912.425, 'text': "That's a prerequisite for this class.", 'start': 1910.984, 'duration': 1.441}, {'end': 1915.427, 'text': 'That there is just a matrix multiplication, which you should be familiar with.', 'start': 1912.785, 'duration': 2.642}, {'end': 1919.389, 'text': "And basically, we're taking x, which is a 3, 072 dimensional column vector.", 'start': 1916.007, 'duration': 3.382}, {'end': 1921.15, 'text': "We're trying to get 10 numbers out.", 'start': 1919.93, 'duration': 1.22}, {'end': 1922.551, 'text': "And it's a linear function.", 'start': 1921.691, 'duration': 0.86}, {'end': 1927.074, 'text': 'So you can go backwards and figure out that the dimensions of this w are basically 10 by 3, 072.', 'start': 1922.692, 'duration': 4.382}, {'end': 1933.439, 'text': 'So there are 30, 7200 numbers that goes into w.', 'start': 1927.074, 'duration': 6.365}, {'end': 1934.459, 'text': "And that's what we have control over.", 'start': 1933.439, 'duration': 1.02}, {'end': 1937.201, 'text': "That's what we have to tweak and find what works best in our data.", 'start': 1934.78, 'duration': 2.421}, {'end': 1939.583, 'text': 'So those are the parameters in this particular case.', 'start': 1937.822, 'duration': 1.761}, {'end': 1945.249, 'text': "What I'm leaving out is there's also an appended plus b sometimes, so you have a bias.", 'start': 1940.704, 'duration': 4.545}, {'end': 1950.916, 'text': 'These biases are, again, 10 more parameters, and we have to also find those.', 'start': 1946.03, 'duration': 4.886}, {'end': 1953.8, 'text': 'So usually in a linear classifier you have a w and a b.', 'start': 1950.956, 'duration': 2.844}, {'end': 1955.181, 'text': 'We have to find exactly what works best.', 'start': 1953.8, 'duration': 1.381}, {'end': 1957.462, 'text': 'And this b is not a function of the image.', 'start': 1955.842, 'duration': 1.62}, {'end': 1963.383, 'text': "That's just independent weights on how likely any one of those images might be.", 'start': 1957.502, 'duration': 5.881}, {'end': 1971.565, 'text': 'So, to go back to your question, if you have a very unbalanced data set for, so maybe you have mostly cats but some dogs or something like that,', 'start': 1963.784, 'duration': 7.781}, {'end': 1976.066, 'text': 'then you might expect that the bias for the cat class might be slightly higher.', 'start': 1971.565, 'duration': 4.501}, {'end': 1981.387, 'text': 'Because by default, the classifier wants to predict the cat class unless something convinces it otherwise.', 'start': 1976.366, 'duration': 5.021}, {'end': 1984.994, 'text': 'Something in the image would convince it otherwise.', 'start': 1983.553, 'duration': 1.441}, {'end': 1988.518, 'text': "So to make this more concrete, I'd just like to break it down.", 'start': 1986.056, 'duration': 2.462}, {'end': 1991.821, 'text': "But of course, I can't visualize it very explicitly with 3, 072 numbers.", 'start': 1988.558, 'duration': 3.263}, {'end': 1996.185, 'text': 'So imagine that our input image only had four pixels.', 'start': 1992.462, 'duration': 3.723}, {'end': 1999.689, 'text': 'So four pixels are stretched out in the column x.', 'start': 1996.666, 'duration': 3.023}, {'end': 2004.273, 'text': 'And imagine that we have three classes, so red, green, and blue class, or a cat, dog, ship class.', 'start': 1999.689, 'duration': 4.584}, {'end': 2008.964, 'text': 'So in this case, w will be only a 3 by 4 matrix.', 'start': 2006.002, 'duration': 2.962}], 'summary': 'Using matrix vector operations to transform 3,072 pixel image into 10 numbers with 30,7200 parameters to tweak for best fit, including 10 bias parameters.', 'duration': 114.381, 'max_score': 1894.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1894583.jpg'}, {'end': 2008.964, 'src': 'embed', 'start': 1963.784, 'weight': 2, 'content': [{'end': 1971.565, 'text': 'So, to go back to your question, if you have a very unbalanced data set for, so maybe you have mostly cats but some dogs or something like that,', 'start': 1963.784, 'duration': 7.781}, {'end': 1976.066, 'text': 'then you might expect that the bias for the cat class might be slightly higher.', 'start': 1971.565, 'duration': 4.501}, {'end': 1981.387, 'text': 'Because by default, the classifier wants to predict the cat class unless something convinces it otherwise.', 'start': 1976.366, 'duration': 5.021}, {'end': 1984.994, 'text': 'Something in the image would convince it otherwise.', 'start': 1983.553, 'duration': 1.441}, {'end': 1988.518, 'text': "So to make this more concrete, I'd just like to break it down.", 'start': 1986.056, 'duration': 2.462}, {'end': 1991.821, 'text': "But of course, I can't visualize it very explicitly with 3, 072 numbers.", 'start': 1988.558, 'duration': 3.263}, {'end': 1996.185, 'text': 'So imagine that our input image only had four pixels.', 'start': 1992.462, 'duration': 3.723}, {'end': 1999.689, 'text': 'So four pixels are stretched out in the column x.', 'start': 1996.666, 'duration': 3.023}, {'end': 2004.273, 'text': 'And imagine that we have three classes, so red, green, and blue class, or a cat, dog, ship class.', 'start': 1999.689, 'duration': 4.584}, {'end': 2008.964, 'text': 'So in this case, w will be only a 3 by 4 matrix.', 'start': 2006.002, 'duration': 2.962}], 'summary': 'Imbalanced data may lead to bias for majority class.', 'duration': 45.18, 'max_score': 1963.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1963784.jpg'}, {'end': 2124.438, 'src': 'embed', 'start': 2095.482, 'weight': 4, 'content': [{'end': 2100.985, 'text': 'What does a linear classifier do in English? We saw the functional form.', 'start': 2095.482, 'duration': 5.503}, {'end': 2102.086, 'text': "It's taking these images.", 'start': 2101.325, 'duration': 0.761}, {'end': 2103.527, 'text': "It's doing this funny operation there.", 'start': 2102.186, 'duration': 1.341}, {'end': 2115.09, 'text': 'But how do we really interpret in English somehow what this is doing? What is this functional form really doing? Yeah, go ahead.', 'start': 2103.567, 'duration': 11.523}, {'end': 2124.438, 'text': "If you just think of it as a single binary classifier, it's basically drawing a line that will tell you, based on the observations in your data,", 'start': 2115.11, 'duration': 9.328}], 'summary': 'A linear classifier draws a line to classify observations in the data.', 'duration': 28.956, 'max_score': 2095.482, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2095482.jpg'}, {'end': 2228.836, 'src': 'embed', 'start': 2201.96, 'weight': 3, 'content': [{'end': 2207.041, 'text': "if we have zero weights, then the classifier doesn't care what's in part of the image.", 'start': 2201.96, 'duration': 5.081}, {'end': 2210.781, 'text': 'So if I have zero weights for this part here, then nothing affects it.', 'start': 2207.061, 'duration': 3.72}, {'end': 2215.362, 'text': "But for some other parts of the image, if you have positive or negative weights, something's going to happen there.", 'start': 2211.101, 'duration': 4.261}, {'end': 2217.183, 'text': 'And this is going to contribute to the score.', 'start': 2215.422, 'duration': 1.761}, {'end': 2219.923, 'text': 'Any other ways of describing it? Yeah.', 'start': 2217.803, 'duration': 2.12}, {'end': 2228.836, 'text': 'taking something that exists in image space and projecting it into a space of labels? Yeah, so you can think about it.', 'start': 2220.649, 'duration': 8.187}], 'summary': 'Zero weights in classifier ignore parts of image, while positive or negative weights affect the score.', 'duration': 26.876, 'max_score': 2201.96, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2201960.jpg'}], 'start': 1803.237, 'title': 'Image classification function', 'summary': 'Discusses constructing a function to classify images into 10 classes using linear functions, neural networks, and convolutional neural networks, with a weight matrix of 30,7200 numbers, aiming to produce correct answers for every image in the training data. it also covers the concept of linear classifiers using a 4-pixel image and a 3 by 4 matrix, computing scores for different classes, and the interpretation of linear classifiers as drawing lines or using template images, emphasizing the impact of weights on spatial positions in the image.', 'chapters': [{'end': 1984.994, 'start': 1803.237, 'title': 'Image classification function', 'summary': 'Discusses constructing a function to classify images into 10 classes, using linear functions, neural networks, and convolutional neural networks, with 30,7200 numbers in the weight matrix, aiming to produce correct answers for every image in the training data.', 'duration': 181.757, 'highlights': ['Constructing a function to classify images into 10 classes The chapter focuses on building a function that takes an image and produces scores for 10 classes.', 'Linear function with 30,7200 numbers in the weight matrix The initial approach involves a linear classification function with a weight matrix of 30,7200 numbers, aiming to produce the correct answers for every image in the training data.', 'Adjusting biases for unbalanced data sets In cases of unbalanced data sets, biases are adjusted to account for the likelihood of certain classes, such as increasing the bias for the class with more instances.']}, {'end': 2303.948, 'start': 1986.056, 'title': 'Linear classifier and image interpretation', 'summary': 'Covers the concept of linear classifiers using a 4-pixel image and a 3 by 4 matrix, with the aim of computing scores for different classes. it discusses the interpretation of linear classifiers as drawing lines or using template images, and the mapping from image space to label space, emphasizing the impact of weights on spatial positions in the image.', 'duration': 317.892, 'highlights': ['The chapter discusses the concept of linear classifiers using a 4-pixel image and a 3 by 4 matrix, aiming to compute scores for different classes. 4-pixel image, 3 by 4 matrix, computing scores for different classes', 'It covers the interpretation of linear classifiers as drawing lines or using template images, and the mapping from image space to label space. interpretation as drawing lines, using template images, mapping from image space to label space', 'The impact of weights on spatial positions in the image is emphasized, with discussion on how weights can signify the importance of different spatial positions in the image. impact of weights on spatial positions, importance of different spatial positions in the image']}], 'duration': 500.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE1803237.jpg', 'highlights': ['Constructing a function to classify images into 10 classes The chapter focuses on building a function that takes an image and produces scores for 10 classes.', 'Linear function with 30,7200 numbers in the weight matrix The initial approach involves a linear classification function with a weight matrix of 30,7200 numbers, aiming to produce the correct answers for every image in the training data.', 'The chapter discusses the concept of linear classifiers using a 4-pixel image and a 3 by 4 matrix, aiming to compute scores for different classes. 4-pixel image, 3 by 4 matrix, computing scores for different classes', 'The impact of weights on spatial positions in the image is emphasized, with discussion on how weights can signify the importance of different spatial positions in the image.', 'It covers the interpretation of linear classifiers as drawing lines or using template images, and the mapping from image space to label space. interpretation as drawing lines, using template images, mapping from image space to label space', 'Adjusting biases for unbalanced data sets In cases of unbalanced data sets, biases are adjusted to account for the likelihood of certain classes, such as increasing the bias for the class with more instances.']}, {'end': 3440.248, 'segs': [{'end': 2374.395, 'src': 'embed', 'start': 2343.417, 'weight': 0, 'content': [{'end': 2347.714, 'text': 'Because we want to ensure that all of them are kind of comparable, of the same stuff,', 'start': 2343.417, 'duration': 4.297}, {'end': 2352.047, 'text': 'so that we can make these columns and we can analyze statistical patterns that are aligned in the image space.', 'start': 2347.714, 'duration': 4.333}, {'end': 2359.491, 'text': 'Yeah In fact, state of the art methods, the way they actually work on this is they always work on square images.', 'start': 2353.03, 'duration': 6.461}, {'end': 2364.793, 'text': 'So if you have a very long image, these methods will actually work worse because many of them, what they do is just squash it.', 'start': 2359.591, 'duration': 5.202}, {'end': 2365.693, 'text': "That's what we do.", 'start': 2365.193, 'duration': 0.5}, {'end': 2367.193, 'text': 'Still works fairly well.', 'start': 2366.293, 'duration': 0.9}, {'end': 2374.395, 'text': 'So yeah, if you have very long pan around images and you try to put that somewhere on some online service, chances are it might work worse,', 'start': 2368.654, 'duration': 5.741}], 'summary': 'State-of-the-art methods work better on square images, not long ones.', 'duration': 30.978, 'max_score': 2343.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2343417.jpg'}, {'end': 2487.997, 'src': 'heatmap', 'start': 2413.388, 'weight': 0.927, 'content': [{'end': 2415.688, 'text': 'And these weights are, we get to choose those eventually.', 'start': 2413.388, 'duration': 2.3}, {'end': 2417.429, 'text': "But it's just a giant weighted sum.", 'start': 2416.029, 'duration': 1.4}, {'end': 2419.69, 'text': "Really all it's doing is it's counting up colors.", 'start': 2417.929, 'duration': 1.761}, {'end': 2423.291, 'text': "It's counting up colors at different spatial positions.", 'start': 2420.47, 'duration': 2.821}, {'end': 2429.917, 'text': 'So one way that was brought up in terms of how we can interpret this W classifier to make it concrete,', 'start': 2424.071, 'duration': 5.846}, {'end': 2431.999, 'text': "is that it's kind of like a template matching thing.", 'start': 2429.917, 'duration': 2.082}, {'end': 2439.286, 'text': "So here what I've done is I've trained a classifier, and I haven't shown you how to do that yet, but I've trained my weight matrix W.", 'start': 2432.579, 'duration': 6.707}, {'end': 2440.647, 'text': "And then I'll come back to this in a second.", 'start': 2439.286, 'duration': 1.361}, {'end': 2444.791, 'text': "I'm taking out every single one of those rows that we've learned, every single classifier,", 'start': 2441.028, 'duration': 3.763}, {'end': 2447.694, 'text': "and I'm reshaping it back to an image so that I can visualize it.", 'start': 2444.791, 'duration': 2.903}, {'end': 2452.037, 'text': "So I'm taking it originally just a giant row of 3, 072 numbers.", 'start': 2448.775, 'duration': 3.262}, {'end': 2454.898, 'text': "I reshape it back to the image to undo the distortion I've done.", 'start': 2452.257, 'duration': 2.641}, {'end': 2457.199, 'text': 'And then I have all these templates.', 'start': 2455.479, 'duration': 1.72}, {'end': 2461.602, 'text': 'And so for example, what you see here is that plane is like a blue blob here.', 'start': 2458.1, 'duration': 3.502}, {'end': 2467.545, 'text': 'The reason you see blue blob is that if you looked at the color channels of this plane template,', 'start': 2462.242, 'duration': 5.303}, {'end': 2470.206, 'text': "you'll see that in the blue channel you'll have lots of positive weights.", 'start': 2467.545, 'duration': 2.661}, {'end': 2476.55, 'text': 'Because those positive weights, if they see blue values, then they interact with those and they get a little contribution to the score.', 'start': 2470.406, 'duration': 6.144}, {'end': 2482.153, 'text': 'So this plane classifier is really just counting up the amount of blue stuff in the image across all these spatial locations.', 'start': 2477.35, 'duration': 4.803}, {'end': 2487.997, 'text': 'And if you look at the red and the green channel for the plane classifier, you might find zero values or even negative values.', 'start': 2482.814, 'duration': 5.183}], 'summary': 'The weight matrix w acts as a template matching tool, counting colors at different positions in images.', 'duration': 74.609, 'max_score': 2413.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2413388.jpg'}, {'end': 2444.791, 'src': 'embed', 'start': 2416.029, 'weight': 3, 'content': [{'end': 2417.429, 'text': "But it's just a giant weighted sum.", 'start': 2416.029, 'duration': 1.4}, {'end': 2419.69, 'text': "Really all it's doing is it's counting up colors.", 'start': 2417.929, 'duration': 1.761}, {'end': 2423.291, 'text': "It's counting up colors at different spatial positions.", 'start': 2420.47, 'duration': 2.821}, {'end': 2429.917, 'text': 'So one way that was brought up in terms of how we can interpret this W classifier to make it concrete,', 'start': 2424.071, 'duration': 5.846}, {'end': 2431.999, 'text': "is that it's kind of like a template matching thing.", 'start': 2429.917, 'duration': 2.082}, {'end': 2439.286, 'text': "So here what I've done is I've trained a classifier, and I haven't shown you how to do that yet, but I've trained my weight matrix W.", 'start': 2432.579, 'duration': 6.707}, {'end': 2440.647, 'text': "And then I'll come back to this in a second.", 'start': 2439.286, 'duration': 1.361}, {'end': 2444.791, 'text': "I'm taking out every single one of those rows that we've learned, every single classifier,", 'start': 2441.028, 'duration': 3.763}], 'summary': 'The classifier counts colors at different positions, functioning like template matching.', 'duration': 28.762, 'max_score': 2416.029, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2416029.jpg'}, {'end': 2601.48, 'src': 'embed', 'start': 2572.531, 'weight': 2, 'content': [{'end': 2575.753, 'text': "We're giving them more power to actually carry out this classification more properly.", 'start': 2572.531, 'duration': 3.222}, {'end': 2577.334, 'text': "But for now, we're constrained by this.", 'start': 2576.113, 'duration': 1.221}, {'end': 2594.158, 'text': "Question? Yes, so what you're referring to, I think, is something we call data augmentation.", 'start': 2578.195, 'duration': 15.963}, {'end': 2600.4, 'text': "So at training time we would not be taking just exact images, but we'll be jittering them, stretching them, skewing them,", 'start': 2594.738, 'duration': 5.662}, {'end': 2601.48, 'text': "and we'll be piping all of that in.", 'start': 2600.4, 'duration': 1.08}], 'summary': 'Enhancing image classification by using data augmentation techniques at training time.', 'duration': 28.949, 'max_score': 2572.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2572531.jpg'}, {'end': 2794.827, 'src': 'heatmap', 'start': 2755.77, 'weight': 0.706, 'content': [{'end': 2758.413, 'text': 'But we have to go into the loss function to exactly see how that will play out.', 'start': 2755.77, 'duration': 2.643}, {'end': 2759.996, 'text': "So it's hard to say right now.", 'start': 2758.433, 'duration': 1.563}, {'end': 2765.872, 'text': 'OK, Another interpretation of the linear classifier that also someone else pointed out.', 'start': 2761.849, 'duration': 4.023}, {'end': 2773.416, 'text': "that I'd like to point out is you can think of these images as very high dimensional points in a 3, 072 dimensional space, right?", 'start': 2765.872, 'duration': 7.544}, {'end': 2778.179, 'text': 'In the 3, 072 pixel space, dimensional pixel space, every image is a point.', 'start': 2773.757, 'duration': 4.422}, {'end': 2783.923, 'text': 'And these linear classifiers are describing these gradients across the 3, 072 dimensional space.', 'start': 2778.78, 'duration': 5.143}, {'end': 2788.646, 'text': 'These scores are this gradient of negative to positive along some linear direction across the space.', 'start': 2783.963, 'duration': 4.683}, {'end': 2794.827, 'text': "And so, for example, here for a car classifier, I'm taking the first row of w, which is the car class,", 'start': 2789.426, 'duration': 5.401}], 'summary': 'Linear classifier interprets images as high-dimensional points in a 3,072 dimensional space and describes gradients across the space.', 'duration': 39.057, 'max_score': 2755.77, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2755770.jpg'}, {'end': 3430.683, 'src': 'embed', 'start': 3403.16, 'weight': 4, 'content': [{'end': 3408.585, 'text': "A neural network, basically what we'll do is you can look at it as stacking linear classifiers to some degree.", 'start': 3403.16, 'duration': 5.425}, {'end': 3414.07, 'text': 'So what it will end up doing is it will have all these little templates really for red cars, yellow cars, green cars,', 'start': 3408.945, 'duration': 5.125}, {'end': 3416.312, 'text': 'whatever cars going this way or that way or that way.', 'start': 3414.07, 'duration': 2.242}, {'end': 3418.935, 'text': 'There will be a neuron assigned to detecting every one of these different modes.', 'start': 3416.352, 'duration': 2.583}, {'end': 3421.577, 'text': 'And then they will be combined across them on a second layer.', 'start': 3419.375, 'duration': 2.202}, {'end': 3424.719, 'text': "So basically, you'll have these neurons looking for different types of cars.", 'start': 3422.518, 'duration': 2.201}, {'end': 3430.683, 'text': "And then the next neuron will be just like, OK, I just take a weight at some of you guys, and I'm just doing an OR operation over you.", 'start': 3424.879, 'duration': 5.804}], 'summary': 'Neural network uses stacked linear classifiers to detect different types of cars and performs or operations over them.', 'duration': 27.523, 'max_score': 3403.16, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE3403160.jpg'}], 'start': 2303.968, 'title': 'Image analysis and interpretation', 'summary': 'Discusses the importance of resizing images for statistical analysis, interpreting the weight matrix in image classification, and the limitations of linear classifiers in capturing diverse image features. it also highlights the impact of data augmentation on classifier performance and the challenges of using grayscale images.', 'chapters': [{'end': 2374.395, 'start': 2303.968, 'title': 'Resizing images for analysis', 'summary': 'Discusses the importance of resizing images to the same size for statistical analysis and mentions that state-of-the-art methods work on square images.', 'duration': 70.427, 'highlights': ['State-of-the-art methods work on square images, ensuring better performance compared to long images.', 'Resizing images to the exact same size is important for ensuring comparability and analyzing statistical patterns.', 'The chapter addresses the challenge of dealing with different sizes of images in a dataset and emphasizes the simplicity of resizing all images to the same size.']}, {'end': 2615.418, 'start': 2374.395, 'title': 'Interpreting w classifier in image recognition', 'summary': 'Discusses interpreting the weight matrix w in image classification, highlighting the concept of template matching, the impact of data augmentation on classifier performance, and the limitations of traditional classifiers in handling variations in image data.', 'duration': 241.023, 'highlights': ['Data augmentation involves jittering, stretching, and skewing training images to create additional training examples, which significantly improves the performance of comnets.', 'The weight matrix W in image classification acts as a template for recognizing specific features in images, such as colors and spatial positions, thereby aiding in the classification process.', 'Traditional classifiers may struggle to handle variations in image data, leading to suboptimal templates for certain classes, such as the two-headed horse and the non-ideal car template.']}, {'end': 3440.248, 'start': 2616.299, 'title': 'Linear classifier and template image analysis', 'summary': 'Discusses the limitations of linear classifiers in capturing diverse image features, the interpretation of linear classifiers in high-dimensional spaces, the challenges of using grayscale images, the need for loss function optimization, and the transition towards more complex neural networks. the limitations of linear classifiers in handling color bias, spatial layout, and texture variations are highlighted.', 'duration': 823.949, 'highlights': ['Linear classifiers struggle with capturing diverse image features such as color bias, spatial layout, and texture variations, thus motivating the need for more complex models like neural networks.', 'The interpretation of linear classifiers involves describing gradients across high-dimensional spaces and visualizing the shifting and turning of classifiers during optimization, highlighting their limitations in capturing complex image features.', 'Using grayscale images with linear classifiers may lead to poor performance as they cannot effectively capture colors, textures, and fine details, impacting classification accuracy.', 'The chapter emphasizes the importance of defining a loss function to quantify the performance of weights across the test set and the process of optimization to find weights that minimize the loss.', 'The challenges of linear classifiers in handling color bias and the potential of neural networks in detecting diverse image features through stacked templates and combined neuron operations are discussed.']}], 'duration': 1136.28, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/8inugqHkfvE/pics/8inugqHkfvE2303968.jpg', 'highlights': ['Resizing images to the exact same size is important for ensuring comparability and analyzing statistical patterns.', 'State-of-the-art methods work on square images, ensuring better performance compared to long images.', 'Data augmentation involves jittering, stretching, and skewing training images to create additional training examples, which significantly improves the performance of comnets.', 'The weight matrix W in image classification acts as a template for recognizing specific features in images, such as colors and spatial positions, thereby aiding in the classification process.', 'Linear classifiers struggle with capturing diverse image features such as color bias, spatial layout, and texture variations, thus motivating the need for more complex models like neural networks.']}], 'highlights': ['The lecture covers machine learning assignments due on January 20, discussing challenges in image classification, the implementation of k-nearest neighbor classifier, and the use of linear classifiers, neural networks, and convolutional neural networks to classify images into 10 classes, emphasizing the impact of weights on spatial positions in the image.', 'The first assignment involves writing a k-nearest neighbor classifier, a linear classifier, and a small two-layer neural network, along with the entirety of back propagation algorithm for a two-layer neural network, due on January 20.', 'The chapter emphasizes the use of Python and NumPy for computation, as well as offering terminal.com for virtual machines in the cloud, with credits being distributed to students for usage.', 'Students are advised to be familiar with Python and optimized NumPy expressions for efficient manipulation of matrices and vectors, with a tutorial available on the website for reference.', 'The data-driven approach in machine learning allows training models to classify new test data.', 'The nearest neighbor classifier compares test images to every image in the training set for practical application in image recognition tasks.', 'The boundaries of the nearest neighbor classifier start to smooth out as the k value increases, resulting in better performance at test time due to the overwhelming influence of multiple nearest neighbors, making k a crucial hyperparameter for the classifier.', 'Cross-validation is essential for setting hyperparameters and ensuring generalization, as different hyperparameters may work better for different applications.', 'The challenges of using distances in high dimensional objects for image classification are discussed, highlighting the limitations of the nearest neighbor classifier in distinguishing between settings.', 'The importance of picking hyperparameters and the use of cross-validation for this purpose is emphasized, with a mention of the common practice of using a single validation set for hyperparameter selection.', 'The process of evaluating the best hyperparameters on a test set is mentioned as a common practice in image classification.', 'The introduction of the nearest-neighbor classifier and the concept of different data splits are highlighted as key points in the discussion.', 'The chapter emphasizes the motivation for the computer vision class and highlights the versatility of neural networks in domains like speech recognition and machine translation.', 'The modular nature of neural networks is likened to LEGO blocks, enabling the easy creation of architectures for various tasks.', 'The image captioning model is detailed, involving the use of convolutional and recurrent neural networks, with the gradients flowing through the convolutional network to improve image understanding.', 'Constructing a function to classify images into 10 classes The chapter focuses on building a function that takes an image and produces scores for 10 classes.', 'Linear function with 30,7200 numbers in the weight matrix The initial approach involves a linear classification function with a weight matrix of 30,7200 numbers, aiming to produce the correct answers for every image in the training data.', 'The chapter discusses the concept of linear classifiers using a 4-pixel image and a 3 by 4 matrix, aiming to compute scores for different classes. 4-pixel image, 3 by 4 matrix, computing scores for different classes', 'The impact of weights on spatial positions in the image is emphasized, with discussion on how weights can signify the importance of different spatial positions in the image.', 'It covers the interpretation of linear classifiers as drawing lines or using template images, and the mapping from image space to label space. interpretation as drawing lines, using template images, mapping from image space to label space', 'Adjusting biases for unbalanced data sets In cases of unbalanced data sets, biases are adjusted to account for the likelihood of certain classes, such as increasing the bias for the class with more instances.', 'Resizing images to the exact same size is important for ensuring comparability and analyzing statistical patterns.', 'State-of-the-art methods work on square images, ensuring better performance compared to long images.', 'Data augmentation involves jittering, stretching, and skewing training images to create additional training examples, which significantly improves the performance of comnets.', 'The weight matrix W in image classification acts as a template for recognizing specific features in images, such as colors and spatial positions, thereby aiding in the classification process.', 'Linear classifiers struggle with capturing diverse image features such as color bias, spatial layout, and texture variations, thus motivating the need for more complex models like neural networks.']}