title
Training Model - Deep Learning and Neural Networks with Python and Pytorch p.4

description
In this deep learning with Python and Pytorch tutorial, we'll be actually training this neural network by learning how to iterate over our data, pass to the model, calculate loss from the result, and then do backpropagation to slowly fit our model to the data. Text-based tutorials and sample code: https://pythonprogramming.net/training-deep-learning-neural-network-pytorch/ Linode Cloud GPUs $20 credit: https://linode.com/sentdex Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex #pytorch #deeplearning #machinelearning

detail
{'title': 'Training Model - Deep Learning and Neural Networks with Python and Pytorch p.4', 'heatmap': [{'end': 179.591, 'start': 148.012, 'weight': 0.831}, {'end': 706.877, 'start': 666.454, 'weight': 0.915}], 'summary': 'Covers training a deep learning model to recognize handwritten digits using pytorch, exploring optimizer roles, transfer learning, learning rate impact on minimizing loss, back propagation, and achieving 97.5% accuracy with neural networks for image recognition.', 'chapters': [{'end': 101.041, 'segs': [{'end': 49.594, 'src': 'embed', 'start': 20.484, 'weight': 0, 'content': [{'end': 23.505, 'text': 'we passed some data through that neural network and got a response.', 'start': 20.484, 'duration': 3.021}, {'end': 27.366, 'text': 'But now what we want to talk about is actually how do we pass through, you know,', 'start': 24.005, 'duration': 3.361}, {'end': 33.508, 'text': "labeled data and actually train the model to hopefully be able to recognize whatever it is we're passing.", 'start': 27.366, 'duration': 6.142}, {'end': 35.248, 'text': "So in this case, it's handwritten digits.", 'start': 33.548, 'duration': 1.7}, {'end': 44.171, 'text': "So the idea is to get this model to the point where we can show it at digits it's never seen before and hopefully it can predict and recognize.", 'start': 35.749, 'duration': 8.422}, {'end': 46.792, 'text': "hey, that's a seven or a three, or whatever it is.", 'start': 44.171, 'duration': 2.621}, {'end': 49.594, 'text': "So, yeah, let's get started.", 'start': 47.632, 'duration': 1.962}], 'summary': 'Training a model to recognize handwritten digits for prediction.', 'duration': 29.11, 'max_score': 20.484, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM20484.jpg'}, {'end': 87.497, 'src': 'embed', 'start': 58.744, 'weight': 1, 'content': [{'end': 62.627, 'text': 'So when it comes to loss, this is just a measure of.', 'start': 58.744, 'duration': 3.883}, {'end': 69.05, 'text': 'How wrong is the model? So our goal over time is to have loss decrease.', 'start': 63.428, 'duration': 5.622}, {'end': 77.373, 'text': "So, even if a model predicts correctly, you know, in terms of argmax or whatever the output is, even if that's correct,", 'start': 69.79, 'duration': 7.583}, {'end': 80.614, 'text': 'chances are the model was at least wrong in some way.', 'start': 77.373, 'duration': 3.241}, {'end': 82.215, 'text': "It wasn't perfect.", 'start': 80.634, 'duration': 1.581}, {'end': 87.497, 'text': "It wasn't 100% confident in any of its predictions, and maybe It was 60% confident.", 'start': 82.255, 'duration': 5.242}], 'summary': 'The goal is to decrease loss over time, even if the model is not 100% confident in its predictions.', 'duration': 28.753, 'max_score': 58.744, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM58744.jpg'}], 'start': 1.969, 'title': 'Training deep learning model', 'summary': 'Covers training a deep learning model to recognize handwritten digits using pytorch, discussing the concepts of loss and optimizer, aiming for the model to decrease loss over time.', 'chapters': [{'end': 101.041, 'start': 1.969, 'title': 'Training deep learning model', 'summary': 'Covers training a deep learning model to recognize handwritten digits using pytorch, discussing the concepts of loss and optimizer and aiming for the model to decrease loss over time.', 'duration': 99.072, 'highlights': ['The chapter covers training a deep learning model to recognize handwritten digits using PyTorch The tutorial focuses on training a model to recognize handwritten digits using PyTorch and Python.', 'Discussing the concepts of loss and optimizer The concept of loss is introduced as a measure of how wrong the model is, and the need for the model to decrease loss over time is emphasized. The concept of an optimizer is also mentioned.', 'Aiming for the model to decrease loss over time The goal is to have the model decrease loss over time, even if it predicts correctly, in order to improve its confidence and accuracy in predictions.']}], 'duration': 99.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1969.jpg', 'highlights': ['The chapter covers training a deep learning model to recognize handwritten digits using PyTorch and Python.', 'The concept of loss is introduced as a measure of how wrong the model is, and the need for the model to decrease loss over time is emphasized.', 'The goal is to have the model decrease loss over time, even if it predicts correctly, in order to improve its confidence and accuracy in predictions.']}, {'end': 312.787, 'segs': [{'end': 179.591, 'src': 'heatmap', 'start': 101.161, 'weight': 0, 'content': [{'end': 107.344, 'text': 'And then the optimizer, what its job is, is to go through and adjust the weights based on the loss, based on these gradients.', 'start': 101.161, 'duration': 6.183}, {'end': 113.446, 'text': 'It wants to optimize and adjust all of the possible weights that it can adjust.', 'start': 109.124, 'duration': 4.322}, {'end': 124.354, 'text': 'in such a way so as to lower the loss slowly over time, and that slowly over time is based on the learning rate that we use.', 'start': 114.807, 'duration': 9.547}, {'end': 133.322, 'text': "So let's go ahead and make some imports, and then I'm going to briefly show some awesome imagery of learning rate.", 'start': 125.455, 'duration': 7.867}, {'end': 145.691, 'text': "So import torch.optim as optim and then we're going to say the optimizer equals optim.atom. And then Adam is going to take for now two parameters.", 'start': 133.722, 'duration': 11.969}, {'end': 147.952, 'text': 'The first is going to be net dot parameters.', 'start': 145.751, 'duration': 2.201}, {'end': 154.175, 'text': "What's this? This corresponds to everything that is adjustable in our model.", 'start': 148.012, 'duration': 6.163}, {'end': 158.617, 'text': "So there are things that don't necessarily have to be adjustable.", 'start': 154.235, 'duration': 4.382}, {'end': 165.681, 'text': 'So there might be layers in your neural network, and this is going to be true, especially if you do transfer learning.', 'start': 158.718, 'duration': 6.963}, {'end': 172.426, 'text': "Where maybe you've got a model that's been trained on millions of images to detect classification or whatever.", 'start': 166.281, 'duration': 6.145}, {'end': 179.591, 'text': 'Those first few layers are going to be very good at very small and general types of image recognition tasks.', 'start': 173.026, 'duration': 6.565}], 'summary': 'The optimizer adjusts weights to lower loss over time based on learning rate.', 'duration': 53.014, 'max_score': 101.161, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM101161.jpg'}, {'end': 202.666, 'src': 'embed', 'start': 173.026, 'weight': 3, 'content': [{'end': 179.591, 'text': 'Those first few layers are going to be very good at very small and general types of image recognition tasks.', 'start': 173.026, 'duration': 6.565}, {'end': 184.535, 'text': 'And then the later layers are going to be very specific to the exact task that you trained it on.', 'start': 180.052, 'duration': 4.483}, {'end': 195.481, 'text': 'Well, what you could do is use transfer learning and freeze those first few layers and then only let the model or the optimizer adjust weights in like the last layers.', 'start': 184.935, 'duration': 10.546}, {'end': 202.666, 'text': "And we'll talk a little bit more about transfer learning later on, but just know that that's a thing you can do.", 'start': 196.062, 'duration': 6.604}], 'summary': 'Initial layers for general tasks, later layers for specific tasks; transfer learning allows freezing initial layers and adjusting weights in the last layers.', 'duration': 29.64, 'max_score': 173.026, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM173026.jpg'}, {'end': 312.787, 'src': 'embed', 'start': 266.924, 'weight': 5, 'content': [{'end': 272.849, 'text': 'It could probably get to this point and still do pretty good, still be pretty accurate at predicting things.', 'start': 266.924, 'duration': 5.925}, {'end': 274.851, 'text': 'But the goal is to get to this point.', 'start': 272.929, 'duration': 1.922}, {'end': 285.66, 'text': 'The learning rate, in part, dictates the size of the step that your optimizer will take to get to the best place.', 'start': 275.832, 'duration': 9.828}, {'end': 289.404, 'text': 'So anytime you pass data through this neural network, you get a loss.', 'start': 285.7, 'duration': 3.704}, {'end': 303.66, 'text': 'it is entirely calculable to determine what weights do we need for loss to be zero, right? To get perfect accuracy on the data we just sent.', 'start': 291.013, 'duration': 12.647}, {'end': 307.062, 'text': 'That is a simple mathematical operation.', 'start': 304.561, 'duration': 2.501}, {'end': 308.503, 'text': 'We could definitely do that.', 'start': 307.303, 'duration': 1.2}, {'end': 312.787, 'text': "We don't want to do that because if we did that, that would be 100%.", 'start': 308.844, 'duration': 3.943}], 'summary': 'The goal is to optimize the neural network to achieve perfect accuracy, which would be 100%.', 'duration': 45.863, 'max_score': 266.924, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM266924.jpg'}], 'start': 101.161, 'title': 'Neural network optimization principles', 'summary': 'Explores the role of optimizer in adjusting weights based on gradients, using adam optimizer, transfer learning, and the significance of learning rate in minimizing loss and improving accuracy.', 'chapters': [{'end': 154.175, 'start': 101.161, 'title': 'Neural network optimizer', 'summary': 'Discusses the role of the optimizer in adjusting weights based on gradients to lower the loss slowly over time, with a demonstration of using the adam optimizer and its parameters.', 'duration': 53.014, 'highlights': ['The optimizer adjusts weights based on gradients to lower the loss slowly over time, determined by the learning rate used.', 'The role of the optimizer is to optimize and adjust all possible weights to lower the loss.', 'The demonstration includes importing torch.optim as optim and initializing the Adam optimizer with net dot parameters.']}, {'end': 312.787, 'start': 154.235, 'title': 'Neural network optimization and learning rate', 'summary': 'Discusses the concept of adjustable parameters in neural networks, the use of transfer learning to freeze specific layers, and the significance of learning rate in determining the size of the step that the optimizer will take to reach the best place, aiming to achieve the goal of minimizing loss and improving accuracy.', 'duration': 158.552, 'highlights': ['The use of transfer learning and freezing specific layers in a neural network allows for adjusting weights only in the last layers, enhancing model optimization for specific tasks. Transfer learning enables freezing initial layers trained on general image recognition, optimizing later layers for specific tasks, potentially improving model performance.', 'The learning rate in neural network optimization dictates the size of the step taken by the optimizer to reach the best place, impacting training times and model learning. Learning rate determines the step size for optimizer, influencing model learning and convergence towards minimizing loss.', 'The calculation of weights needed for zero loss and perfect accuracy is entirely feasible but not pursued to avoid 100% accuracy. Feasibility of determining weights for zero loss and perfect accuracy exists, but it is intentionally avoided to prevent 100% accuracy, maintaining model generalization.']}], 'duration': 211.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM101161.jpg', 'highlights': ['The optimizer adjusts weights based on gradients to lower the loss slowly over time, determined by the learning rate used.', 'The role of the optimizer is to optimize and adjust all possible weights to lower the loss.', 'The demonstration includes importing torch.optim as optim and initializing the Adam optimizer with net dot parameters.', 'The use of transfer learning and freezing specific layers in a neural network allows for adjusting weights only in the last layers, enhancing model optimization for specific tasks.', 'Transfer learning enables freezing initial layers trained on general image recognition, optimizing later layers for specific tasks, potentially improving model performance.', 'The learning rate in neural network optimization dictates the size of the step taken by the optimizer to reach the best place, impacting training times and model learning.', 'Learning rate determines the step size for optimizer, influencing model learning and convergence towards minimizing loss.', 'The calculation of weights needed for zero loss and perfect accuracy is entirely feasible but not pursued to avoid 100% accuracy.', 'Feasibility of determining weights for zero loss and perfect accuracy exists, but it is intentionally avoided to prevent 100% accuracy, maintaining model generalization.']}, {'end': 594.097, 'segs': [{'end': 373.066, 'src': 'embed', 'start': 312.787, 'weight': 2, 'content': [{'end': 320.332, 'text': "We're gonna overfit to each batch that passes through and we're gonna just keep basically overfitting everything that comes through.", 'start': 312.787, 'duration': 7.545}, {'end': 326.376, 'text': 'So we use learning rate to tell the optimizer, optimize to lower the loss, but only take certain size steps.', 'start': 320.372, 'duration': 6.004}, {'end': 333.68, 'text': 'And then, over time, as you take those steps,', 'start': 326.796, 'duration': 6.884}, {'end': 340.604, 'text': 'the steps that were taken or the changes that were made that were just basically based on just the data passed, will kind of get overwritten.', 'start': 333.68, 'duration': 6.924}, {'end': 347.348, 'text': 'And what will remain as we go batch after batch after batch, what remains for us is the actual general principles.', 'start': 340.984, 'duration': 6.364}, {'end': 349.169, 'text': 'But anyway, I digress.', 'start': 347.508, 'duration': 1.661}, {'end': 351.731, 'text': "Let's talk about the learning rate and step size.", 'start': 349.57, 'duration': 2.161}, {'end': 355.913, 'text': "So let's say you've got this optimizer and you're telling it, hey, learn really fast.", 'start': 351.771, 'duration': 4.142}, {'end': 357.094, 'text': 'Just go as quick as you can.', 'start': 355.953, 'duration': 1.141}, {'end': 357.955, 'text': 'Take huge steps.', 'start': 357.154, 'duration': 0.801}, {'end': 361.677, 'text': "Well, the problem is it's going to take steps like, you know, this big.", 'start': 358.535, 'duration': 3.142}, {'end': 366.28, 'text': 'right, and then it gets here, here, here, and then it just keeps.', 'start': 362.077, 'duration': 4.203}, {'end': 369.223, 'text': "it can't get any lower than that right.", 'start': 366.28, 'duration': 2.943}, {'end': 373.066, 'text': "so if you have a learning rate that's too quick, too big,", 'start': 369.223, 'duration': 3.843}], 'summary': 'Overfitting occurs with high learning rates; smaller steps preserve general principles.', 'duration': 60.279, 'max_score': 312.787, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM312787.jpg'}, {'end': 439.249, 'src': 'embed', 'start': 415.252, 'weight': 1, 'content': [{'end': 422.156, 'text': 'And the way this works is it starts off with these gigantic steps, right? But over time, the learning rate slowly gets smaller and smaller.', 'start': 415.252, 'duration': 6.904}, {'end': 428.862, 'text': 'So it starts off taking these huge steps, might get stuck in this local minimum area, but then it gets smaller right?', 'start': 422.237, 'duration': 6.625}, {'end': 430.843, 'text': 'And then it gets smaller and smaller.', 'start': 429.082, 'duration': 1.761}, {'end': 437.048, 'text': "So basically, the idea is that'll help you descend this kind of mountain, I suppose.", 'start': 431.664, 'duration': 5.384}, {'end': 439.249, 'text': 'and get to where you want to go.', 'start': 438.208, 'duration': 1.041}], 'summary': 'Gradient descent algorithm takes smaller steps over time to reach the desired destination.', 'duration': 23.997, 'max_score': 415.252, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM415252.jpg'}, {'end': 539.62, 'src': 'embed', 'start': 496.122, 'weight': 0, 'content': [{'end': 503.566, 'text': 'We just keep passing data, and then hopefully over time loss falls, and as loss falls, the expectation is that accuracy will also improve.', 'start': 496.122, 'duration': 7.444}, {'end': 509.155, 'text': "But the model actually does not, we don't optimize for accuracy.", 'start': 504.107, 'duration': 5.048}, {'end': 510.417, 'text': 'We optimize for loss.', 'start': 509.375, 'duration': 1.042}, {'end': 512.44, 'text': 'It just so happens that accuracy follows.', 'start': 510.577, 'duration': 1.863}, {'end': 515.184, 'text': "So let's get to it.", 'start': 513.402, 'duration': 1.782}, {'end': 521.254, 'text': 'So what we want to do is we want to iterate over all of our data, pass it through the model.', 'start': 516.972, 'duration': 4.282}, {'end': 526.875, 'text': 'But we also generally want to iterate at least a few times through our data.', 'start': 521.734, 'duration': 5.141}, {'end': 529.997, 'text': "A full pass through our data is what's called an epoch.", 'start': 527.396, 'duration': 2.601}, {'end': 532.057, 'text': "And we're going to say epochs equals three.", 'start': 530.397, 'duration': 1.66}, {'end': 535.338, 'text': "So we're going to make three whole passes through our entire data set.", 'start': 532.117, 'duration': 3.221}, {'end': 539.62, 'text': "So then what I'm going to do is make some space here.", 'start': 536.159, 'duration': 3.461}], 'summary': 'Optimizing for loss, aiming for three epochs to improve accuracy.', 'duration': 43.498, 'max_score': 496.122, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM496122.jpg'}], 'start': 312.787, 'title': 'Learning rate and overfitting', 'summary': 'Discusses the impact of overfitting, the use of learning rate to optimize lower loss, and the emergence of general principles after multiple batches in training neural networks. it also covers the importance of finding the right learning rate, the concept of decaying learning rate, and the process of optimizing loss to improve accuracy in machine learning.', 'chapters': [{'end': 351.731, 'start': 312.787, 'title': 'Understanding learning rate and overfitting', 'summary': 'Discusses the impact of overfitting, the use of learning rate to optimize lower loss, and the gradual emergence of general principles after multiple batches in training neural networks.', 'duration': 38.944, 'highlights': ['Using learning rate to optimize lower loss and prevent overfitting by taking certain size steps', 'The gradual emergence of general principles as the changes based on data passed get overwritten over time', 'The impact of overfitting to each batch that passes through in neural network training']}, {'end': 594.097, 'start': 351.771, 'title': 'Optimizing learning rate for efficiency', 'summary': "Discusses the importance of finding the right learning rate, the concept of decaying learning rate, and the process of optimizing loss to improve accuracy in machine learning. it emphasizes the impact of learning rate on the model's optimization process and the significance of iterating through the dataset multiple times.", 'duration': 242.326, 'highlights': ["The importance of finding the right learning rate and its impact on the optimization process is emphasized, highlighting the consequences of excessively large or small steps on the model's ability to reach the desired point. Finding the right learning rate is crucial for the optimization process; excessively large or small steps can hinder the model's ability to reach the desired point.", "The concept of decaying learning rate is introduced, which involves starting with large steps that gradually become smaller over time to aid in descending the 'mountain' and reaching the desired destination. Decaying learning rate involves starting with large steps that gradually become smaller, aiding in descending the 'mountain' and reaching the desired destination.", 'The process of optimizing loss to improve accuracy is explained, highlighting the iterative nature of adjusting weights based on the loss and the emphasis on optimizing for loss rather than accuracy. The process involves adjusting weights based on the loss to improve accuracy, emphasizing the optimization for loss rather than accuracy.', 'The significance of iterating through the dataset multiple times, referred to as epochs, is emphasized, with the specific example of iterating three times through the entire dataset for optimization. Iterating through the dataset multiple times, known as epochs, is crucial for optimization; the example of iterating three times through the entire dataset is provided.']}], 'duration': 281.31, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM312787.jpg', 'highlights': ['The process of optimizing loss to improve accuracy, emphasizing the optimization for loss rather than accuracy.', "The concept of decaying learning rate, involving starting with large steps that gradually become smaller over time to aid in descending the 'mountain' and reaching the desired destination.", "The importance of finding the right learning rate and its impact on the optimization process is emphasized, highlighting the consequences of excessively large or small steps on the model's ability to reach the desired point.", 'The significance of iterating through the dataset multiple times, known as epochs, is crucial for optimization; the example of iterating three times through the entire dataset is provided.', 'Using learning rate to optimize lower loss and prevent overfitting by taking certain size steps.', 'The gradual emergence of general principles as the changes based on data passed get overwritten over time.', 'The impact of overfitting to each batch that passes through in neural network training.']}, {'end': 1003.14, 'segs': [{'end': 620.663, 'src': 'embed', 'start': 594.297, 'weight': 3, 'content': [{'end': 598.478, 'text': "I'm only using it here because it simplifies it for tutorial purposes initially.", 'start': 594.297, 'duration': 4.181}, {'end': 610.06, 'text': "Anyways, it's a container that contains 10 feature sets, and the feature sets are just grayscale pixel values and then 10 targets labels,", 'start': 598.778, 'duration': 11.282}, {'end': 610.72, 'text': 'whatever you want to call them.', 'start': 610.06, 'duration': 0.66}, {'end': 614.521, 'text': "They're the class that basically says, hey, this is a 3, this is a 7, this is a 9, and so on.", 'start': 610.74, 'duration': 3.781}, {'end': 620.663, 'text': 'so x, y data, so um.', 'start': 617.381, 'duration': 3.282}], 'summary': 'Data contains 10 feature sets and labels for tutorial.', 'duration': 26.366, 'max_score': 594.297, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM594297.jpg'}, {'end': 721.964, 'src': 'heatmap', 'start': 666.454, 'weight': 0, 'content': [{'end': 677.902, 'text': 'Every time we run this kind of, every time we calculate loss and optimize the model, we want to start with kind of a.', 'start': 666.454, 'duration': 11.448}, {'end': 685.648, 'text': 'so every time we actually calculate and make these little gradients so, pretty much every time before you pass data through your neural network,', 'start': 677.902, 'duration': 7.746}, {'end': 688.99, 'text': 'what you want to do is net dot, zero underscore gradient.', 'start': 685.648, 'duration': 3.342}, {'end': 692.369, 'text': 'Now, There, it could be the case.', 'start': 689.811, 'duration': 2.558}, {'end': 695.15, 'text': "So for example, there's two reasons we batch our data.", 'start': 692.409, 'duration': 2.741}, {'end': 698.692, 'text': 'One is it increases or decreases training time.', 'start': 695.591, 'duration': 3.101}, {'end': 706.877, 'text': "So it goes faster right? If we train in batches, but we don't want to pass the entire data set for reasons I brought up before already.", 'start': 698.732, 'duration': 8.145}, {'end': 713.3, 'text': 'but we also like batches because at at There is a law of diminishing returns here.', 'start': 706.877, 'duration': 6.423}, {'end': 720.083, 'text': 'but somewhere between usually 32 and maybe 128 of a batch size also helps to generalize.', 'start': 713.3, 'duration': 6.783}, {'end': 721.964, 'text': "It's kind of two things going for us.", 'start': 720.143, 'duration': 1.821}], 'summary': 'Optimizing model with batch data reduces training time and improves generalization, typically using batch sizes between 32 and 128.', 'duration': 44.062, 'max_score': 666.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM666454.jpg'}, {'end': 818.427, 'src': 'embed', 'start': 790.611, 'weight': 2, 'content': [{'end': 797.054, 'text': 'Like how wrong were you? And then your optimizer goes through and uses those gradients to try to optimize these weights.', 'start': 790.611, 'duration': 6.443}, {'end': 798.995, 'text': 'so net zero grab.', 'start': 797.694, 'duration': 1.301}, {'end': 808.741, 'text': "the next thing we're gonna do is actually pass data through the network, so output is equal to net, and then we pass x dot view negative one.", 'start': 798.995, 'duration': 9.746}, {'end': 809.921, 'text': 'uh, you could also make this.', 'start': 808.741, 'duration': 1.18}, {'end': 818.427, 'text': "you could just say hey, it's my, whatever my batch size is, but in this case negative one's fine, and then it's 28 by 28, so 784..", 'start': 809.921, 'duration': 8.506}], 'summary': 'Using gradients to optimize weights and passing data through network for output.', 'duration': 27.816, 'max_score': 790.611, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM790611.jpg'}, {'end': 940.247, 'src': 'embed', 'start': 912.481, 'weight': 4, 'content': [{'end': 916.904, 'text': 'So when your data is just a scalar value, you have to use a different loss metric.', 'start': 912.481, 'duration': 4.423}, {'end': 918.265, 'text': "You couldn't use mean squared error.", 'start': 916.924, 'duration': 1.341}, {'end': 920.686, 'text': 'So just know going into it.', 'start': 918.645, 'duration': 2.041}, {'end': 923.428, 'text': "just assume, like for now again we'll talk more.", 'start': 920.686, 'duration': 2.742}, {'end': 928.911, 'text': "but if your data set is a scalar value like this, it's not a vector right.", 'start': 923.428, 'duration': 5.483}, {'end': 932.393, 'text': 'with just one hot, just use NLL loss, okay?', 'start': 928.911, 'duration': 3.482}, {'end': 936.176, 'text': 'If your data is a one hot vector, use mean squared error.', 'start': 932.874, 'duration': 3.302}, {'end': 940.247, 'text': 'Cool Like I said, we will talk more on that.', 'start': 937.546, 'duration': 2.701}], 'summary': 'Use nll loss for scalar data, mse for one hot vector.', 'duration': 27.766, 'max_score': 912.481, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM912481.jpg'}, {'end': 989.191, 'src': 'embed', 'start': 958.355, 'weight': 5, 'content': [{'end': 960.656, 'text': 'Now what we want to do is back propagate the loss.', 'start': 958.355, 'duration': 2.301}, {'end': 963.357, 'text': "So we're going to say loss literally dot backward.", 'start': 960.676, 'duration': 2.681}, {'end': 966.419, 'text': 'Done This is magical.', 'start': 964.078, 'duration': 2.341}, {'end': 971.481, 'text': 'Luckily, this is one of the things that PyTorch does just do for us.', 'start': 967.879, 'duration': 3.602}, {'end': 975.603, 'text': "We don't actually have to do it, but you could.", 'start': 971.941, 'duration': 3.662}, {'end': 981.707, 'text': "And again, that's one of the cool things about PyTorch is not only can you do it, it would be pretty simple.", 'start': 976.104, 'duration': 5.603}, {'end': 989.191, 'text': 'You would just iterate over net.parameters, and then you could do at least stochastic gradient descent is pretty simple to do.', 'start': 982.047, 'duration': 7.144}], 'summary': 'Back propagate the loss in pytorch for stochastic gradient descent.', 'duration': 30.836, 'max_score': 958.355, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM958355.jpg'}], 'start': 594.297, 'title': 'Neural network training, optimization, loss, and back propagation', 'summary': 'Covers neural network training with batch processing to improve speed, emphasizing zeroing gradients; also includes loss calculation methods and back propagation using pytorch.', 'chapters': [{'end': 832.933, 'start': 594.297, 'title': 'Neural network training and optimization', 'summary': 'Explains the process of training a neural network, including the use of batches to increase training speed and the importance of zeroing gradients before passing data through the network.', 'duration': 238.636, 'highlights': ['The importance of zeroing gradients before passing data through the neural network to prevent gradients from getting added together, which can impact training accuracy and efficiency.', 'The use of batch training to increase or decrease training speed, with a batch size typically between 32 and 128, which also helps in generalizing the model.', 'Explanation of the process of passing data through the network, including the dimensions of the input image and the calculation of the error or loss to optimize the weights.', 'Description of the container containing 10 feature sets with grayscale pixel values and 10 target labels, providing insights into the structure of the input data for neural network training.']}, {'end': 1003.14, 'start': 833.633, 'title': 'Neural network loss and back propagation', 'summary': 'Discusses the calculation of loss in neural networks, including the use of nll loss for one hot vectors and different loss metrics for scalar values, and the back propagation of loss using pytorch.', 'duration': 169.507, 'highlights': ['Loss calculation for one hot vectors involves using NLL loss, while scalar values require different loss metrics such as mean squared error. One hot vectors require NLL loss, while scalar values necessitate different loss metrics like mean squared error.', "The back propagation of loss is achieved by calling 'loss.backward' in PyTorch, which is automatically handled by the framework but can be customized if needed. Back propagation of loss is executed by calling 'loss.backward' in PyTorch, with the option to customize the process by iterating over net.parameters."]}], 'duration': 408.843, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM594297.jpg', 'highlights': ['Importance of zeroing gradients to prevent accumulation and improve training accuracy and efficiency.', 'Batch training with a typical size between 32 and 128 to increase training speed and model generalization.', 'Explanation of passing data through the network and calculating error/loss for weight optimization.', 'Insights into the structure of input data for neural network training, including grayscale pixel values and target labels.', 'Use of NLL loss for one hot vectors and mean squared error for scalar values in loss calculation.', "Back propagation of loss in PyTorch using 'loss.backward', with the option for customization."]}, {'end': 1430.624, 'segs': [{'end': 1053.523, 'src': 'embed', 'start': 1003.2, 'weight': 0, 'content': [{'end': 1006.363, 'text': 'But for now, we just used loss.backward because we can.', 'start': 1003.2, 'duration': 3.163}, {'end': 1014.471, 'text': "we're going to say optimizer.step, and this is what actually will adjust the weights for us.", 'start': 1007.625, 'duration': 6.846}, {'end': 1016.532, 'text': "and that's basically it.", 'start': 1014.471, 'duration': 2.061}, {'end': 1023.418, 'text': "so what i'm going to say is for data and trade set cool, we'll just come down here and let's, at the end of this,", 'start': 1016.532, 'duration': 6.886}, {'end': 1028.041, 'text': "let's just print loss and this will give us the plus print loss.", 'start': 1023.418, 'duration': 4.623}, {'end': 1032.546, 'text': 'This will actually give us the loss value.', 'start': 1028.402, 'duration': 4.144}, {'end': 1035.608, 'text': 'I know this is not what everybody wants to see at the moment.', 'start': 1033.066, 'duration': 2.542}, {'end': 1040.893, 'text': 'We will talk about doing accuracy, but the goal is to see loss decline.', 'start': 1035.688, 'duration': 5.205}, {'end': 1043.755, 'text': "So I'll just go ahead and get that to start running.", 'start': 1041.492, 'duration': 2.263}, {'end': 1048.199, 'text': 'Again, we are still running on the CPU, so things might not be super fast.', 'start': 1044.214, 'duration': 3.985}, {'end': 1053.523, 'text': 'I will be showing how to get on the GPU and I will eventually be doing everything on the GPU,', 'start': 1048.358, 'duration': 5.165}], 'summary': 'Code adjusts weights with loss.backward and optimizer.step for data and trade set cool.', 'duration': 50.323, 'max_score': 1003.2, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1003200.jpg'}, {'end': 1106.598, 'src': 'embed', 'start': 1082.981, 'weight': 2, 'content': [{'end': 1089.406, 'text': "So the next thing that we're gonna do while we wait on this to train, we can actually start coding the next stuff as this pops out.", 'start': 1082.981, 'duration': 6.425}, {'end': 1090.907, 'text': "There's really no reason why we can't do it.", 'start': 1089.426, 'duration': 1.481}, {'end': 1092.708, 'text': "I'm just kind of surprised at how slow that is.", 'start': 1091.147, 'duration': 1.561}, {'end': 1094.71, 'text': 'I really thought this would go faster.', 'start': 1092.788, 'duration': 1.922}, {'end': 1097.732, 'text': "I've done this on the CPU.", 'start': 1095.91, 'duration': 1.822}, {'end': 1099.393, 'text': "It normally isn't this slow.", 'start': 1097.992, 'duration': 1.401}, {'end': 1102.615, 'text': "anyway. so let's say we want to actually calculate.", 'start': 1099.753, 'duration': 2.862}, {'end': 1106.598, 'text': "i'm just, i'm just trying to think in my head like, did i do something wrong?", 'start': 1102.615, 'duration': 3.983}], 'summary': 'The speaker is surprised at the slow training speed and questions if an error was made.', 'duration': 23.617, 'max_score': 1082.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1082981.jpg'}, {'end': 1171.689, 'src': 'embed', 'start': 1143.726, 'weight': 4, 'content': [{'end': 1150.372, 'text': "so in this case, when we act, when we're trying to validate our data, We actually don't want gradients to be calculated.", 'start': 1143.726, 'duration': 6.646}, {'end': 1152.314, 'text': 'This is supposed to be out of sample data.', 'start': 1150.412, 'duration': 1.902}, {'end': 1153.415, 'text': 'This is testing data.', 'start': 1152.354, 'duration': 1.061}, {'end': 1155.938, 'text': 'We just want to see how right or wrong is the model.', 'start': 1153.896, 'duration': 2.042}, {'end': 1158.56, 'text': "We don't actually want to optimize based on this data.", 'start': 1156.218, 'duration': 2.342}, {'end': 1162.484, 'text': "So we're saying with torch.nograd because we don't want to count gradients here.", 'start': 1158.64, 'duration': 3.844}, {'end': 1165.226, 'text': 'We just want to know how good is the network at this point.', 'start': 1162.824, 'duration': 2.402}, {'end': 1171.689, 'text': "So we're gonna say with oh and Again, I'm not a pie torch expert.", 'start': 1166.367, 'duration': 5.322}], 'summary': 'Validating data without calculating gradients to assess model performance.', 'duration': 27.963, 'max_score': 1143.726, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1143726.jpg'}, {'end': 1218.165, 'src': 'embed', 'start': 1186.453, 'weight': 3, 'content': [{'end': 1191.074, 'text': 'and and what this did was based on your parent class.', 'start': 1186.453, 'duration': 4.621}, {'end': 1194.256, 'text': 'here, i think, is where it was pulling from.', 'start': 1191.074, 'duration': 3.182}, {'end': 1201.838, 'text': 'this would dictate whether your model was in training mode or eval mode, or evaluate, basically to do validation.', 'start': 1194.256, 'duration': 7.582}, {'end': 1207.581, 'text': 'uh, i, at least i never saw this in the actual pytorch documentations that i went through.', 'start': 1201.838, 'duration': 5.743}, {'end': 1208.821, 'text': "i think that's old, like.", 'start': 1207.581, 'duration': 1.24}, {'end': 1210.842, 'text': "i don't think that's a thing you need to do anymore.", 'start': 1208.821, 'duration': 2.021}, {'end': 1218.165, 'text': "i could be wrong, but if you're looking at people's code, historically, that's where i was seeing it in other people's.", 'start': 1211.582, 'duration': 6.583}], 'summary': "Parent class dictates model's training or evaluation mode. not found in current pytorch documentation.", 'duration': 31.712, 'max_score': 1186.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1186453.jpg'}, {'end': 1413.781, 'src': 'embed', 'start': 1371.762, 'weight': 5, 'content': [{'end': 1376.423, 'text': 'If both were one hot or scalar values, I can think of some quicker ways.', 'start': 1371.762, 'duration': 4.661}, {'end': 1383.185, 'text': "But anyways, all we're doing is we're just going to say hey, for every prediction that we made, does it match the actual target value?", 'start': 1376.483, 'duration': 6.702}, {'end': 1384.725, 'text': 'If it does, hey, we got it correct.', 'start': 1383.265, 'duration': 1.46}, {'end': 1385.605, 'text': 'Either way, we total.', 'start': 1384.825, 'duration': 0.78}, {'end': 1388.906, 'text': "So what do we do? So let's go ahead and run this.", 'start': 1385.705, 'duration': 3.201}, {'end': 1394.142, 'text': 'This is also slow.', 'start': 1393.137, 'duration': 1.005}, {'end': 1396.574, 'text': 'We need to be on the GPU bad.', 'start': 1395.027, 'duration': 1.547}, {'end': 1413.781, 'text': "While we wait on that, shout out to long-term channel members Igor Malinov, Faust, Florian Linscheid, Werther's Original, Alan Wang, Daniel Cuchiello,", 'start': 1401.138, 'duration': 12.643}], 'summary': 'Discussing methods to check the accuracy of predictions and the need for gpu support.', 'duration': 42.019, 'max_score': 1371.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1371762.jpg'}], 'start': 1003.2, 'title': 'Calculating loss, adjusting weights, pytorch validation, and model evaluation', 'summary': 'Covers calculating loss, adjusting weights using optimizer.step, transitioning to gpu for faster processing, pytorch model validation, accuracy evaluation, use of torch.nograd, and comparing model predictions with target values.', 'chapters': [{'end': 1102.615, 'start': 1003.2, 'title': 'Calculating loss and adjusting weights', 'summary': 'Demonstrates the process of calculating loss and adjusting weights using optimizer.step, emphasizing the goal of seeing the loss decline while training the model on the cpu and discussing the intention to eventually transition to the gpu for faster processing.', 'duration': 99.415, 'highlights': ['The process of calculating loss and adjusting weights using optimizer.step is demonstrated, with an emphasis on the goal of observing the decline in loss value during training.', 'The intention to eventually transition to the GPU for faster processing is discussed, while currently focusing on introducing one concept at a time.', 'The slow performance on the CPU during training is noted, expressing surprise at the prolonged duration compared to previous experiences.']}, {'end': 1430.624, 'start': 1102.615, 'title': 'Pytorch validation and model evaluation', 'summary': 'Discusses the process of validating a pytorch model and evaluating its accuracy, including the use of torch.nograd and comparing model predictions with target values to calculate accuracy.', 'duration': 328.009, 'highlights': ['The chapter discusses the process of validating a PyTorch model and evaluating its accuracy The transcript covers the steps involved in validating a PyTorch model and evaluating its accuracy', 'including the use of torch.nograd The use of torch.nograd is highlighted as a technique to prevent gradient calculation during model validation', 'comparing model predictions with target values to calculate accuracy The process of comparing model predictions with target values to calculate the accuracy of the model is discussed']}], 'duration': 427.424, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1003200.jpg', 'highlights': ['The process of calculating loss and adjusting weights using optimizer.step is demonstrated, with an emphasis on the goal of observing the decline in loss value during training.', 'The intention to eventually transition to the GPU for faster processing is discussed, while currently focusing on introducing one concept at a time.', 'The slow performance on the CPU during training is noted, expressing surprise at the prolonged duration compared to previous experiences.', 'The chapter discusses the process of validating a PyTorch model and evaluating its accuracy.', 'The use of torch.nograd is highlighted as a technique to prevent gradient calculation during model validation.', 'The process of comparing model predictions with target values to calculate the accuracy of the model is discussed.']}, {'end': 1855.175, 'segs': [{'end': 1511.262, 'src': 'embed', 'start': 1431.405, 'weight': 0, 'content': [{'end': 1436.707, 'text': 'Okay, we got accuracy of 97.5%.', 'start': 1431.405, 'duration': 5.302}, {'end': 1437.568, 'text': 'So we did really well.', 'start': 1436.707, 'duration': 0.861}, {'end': 1439.949, 'text': 'Unless we somehow cheated.', 'start': 1438.908, 'duration': 1.041}, {'end': 1440.729, 'text': "I don't think we did.", 'start': 1439.969, 'duration': 0.76}, {'end': 1446.292, 'text': "But one thing I will tell you is it's really, really easy to cheat with neural networks.", 'start': 1441.81, 'duration': 4.482}, {'end': 1451.414, 'text': "And I don't mean actually cheat, but to miss something little.", 'start': 1446.332, 'duration': 5.082}, {'end': 1457.037, 'text': "So there's lots of ways you can sneak in little biases without realizing what you're doing.", 'start': 1451.494, 'duration': 5.543}, {'end': 1462.299, 'text': "And again, the neural network doesn't understand Oh, I shouldn't actually be using this information.", 'start': 1457.057, 'duration': 5.242}, {'end': 1465.541, 'text': "If it's useful to figuring out what the accuracy is,", 'start': 1462.719, 'duration': 2.822}, {'end': 1476.606, 'text': 'whether it has some direct relationship with the target or it could be something way more obscure than that, like an imbalance issue or whatever.', 'start': 1465.541, 'duration': 11.065}, {'end': 1483.635, 'text': 'Just know that normally seeing an accuracy this high should be a major red flag.', 'start': 1477.393, 'duration': 6.242}, {'end': 1491.317, 'text': 'This is valid for this task, but for most real-life tasks you will never see an accuracy this high,', 'start': 1484.055, 'duration': 7.262}, {'end': 1494.458, 'text': 'especially if the distribution is like 10 classes or something like that.', 'start': 1491.317, 'duration': 3.141}, {'end': 1497.018, 'text': 'This is very difficult to get.', 'start': 1494.778, 'duration': 2.24}, {'end': 1501.34, 'text': 'In this case, it probably is legitimate, but be wary.', 'start': 1497.799, 'duration': 3.541}, {'end': 1503.52, 'text': 'Be very wary.', 'start': 1502.78, 'duration': 0.74}, {'end': 1511.262, 'text': 'So for example, as we iterated, the last batch will still be stored as Xs and Ys.', 'start': 1503.88, 'duration': 7.382}], 'summary': 'Neural network achieved 97.5% accuracy, but warns about potential biases and challenges in real-life tasks.', 'duration': 79.857, 'max_score': 1431.405, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1431405.jpg'}, {'end': 1748.589, 'src': 'embed', 'start': 1724.597, 'weight': 3, 'content': [{'end': 1731.38, 'text': "there's a lot of stuff that was just done for us, that if you tried to take what you know right now and apply it to some other use case,", 'start': 1724.597, 'duration': 6.783}, {'end': 1737.143, 'text': "especially with imagery, but even other kind of tasks that you might want to do, chances are you'd have a really hard time.", 'start': 1731.38, 'duration': 5.763}, {'end': 1744.907, 'text': "so, uh, what we're going to do is leave mnist and leave Cheating behind, and we're gonna grab.", 'start': 1737.143, 'duration': 7.764}, {'end': 1748.589, 'text': "We're still gonna grab a pre-made data set, because building your own data set is absurd.", 'start': 1744.907, 'duration': 3.682}], 'summary': 'Transitioning from mnist and cheating to a pre-made data set for easier application in different use cases.', 'duration': 23.992, 'max_score': 1724.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1724597.jpg'}, {'end': 1791.124, 'src': 'embed', 'start': 1763.338, 'weight': 4, 'content': [{'end': 1767.221, 'text': "in fact, i'm not even sure i'm going to teach recurrent neural networks in this series,", 'start': 1763.338, 'duration': 3.883}, {'end': 1772.745, 'text': 'because the things recurrent neural networks were being used for, like sequences of data,', 'start': 1767.221, 'duration': 5.524}, {'end': 1775.987, 'text': "we're actually kind of finding convolutional neural networks are doing better.", 'start': 1772.745, 'duration': 3.242}, {'end': 1778.409, 'text': "so so we'll see.", 'start': 1775.987, 'duration': 2.422}, {'end': 1779.49, 'text': 'uh, what i decided to do there.', 'start': 1778.409, 'duration': 1.081}, {'end': 1780.59, 'text': "i really haven't made up my mind.", 'start': 1779.49, 'duration': 1.1}, {'end': 1783.454, 'text': "So anyway, that's all for now.", 'start': 1781.551, 'duration': 1.903}, {'end': 1788.22, 'text': "If you've got questions, comments, concerns, whatever, feel free to leave them below.", 'start': 1783.734, 'duration': 4.486}, {'end': 1791.124, 'text': 'Hopefully you guys are enjoying the series.', 'start': 1788.981, 'duration': 2.143}], 'summary': 'Recurrent neural networks may not be taught due to convolutional networks performing better in sequence data.', 'duration': 27.786, 'max_score': 1763.338, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1763338.jpg'}, {'end': 1830.749, 'src': 'embed', 'start': 1803.294, 'weight': 5, 'content': [{'end': 1808.756, 'text': 'There is a library called Ignite, which I guess makes the training loop easier.', 'start': 1803.294, 'duration': 5.462}, {'end': 1813.357, 'text': "Because that's the most tedious thing with PyTorch, is the whole training loop that you've got to code all this.", 'start': 1808.776, 'duration': 4.581}, {'end': 1815.758, 'text': "It's highly likely you'll make a mistake somewhere.", 'start': 1813.777, 'duration': 1.981}, {'end': 1818.26, 'text': 'So, um, there is a package for that.', 'start': 1816.198, 'duration': 2.062}, {'end': 1825.445, 'text': "The other thing is like there's so much stuff that we've we've not taught yet, because I just trying to do a little bit at a time,", 'start': 1818.28, 'duration': 7.165}, {'end': 1830.749, 'text': 'but some of the things that we have to pay attention to are like as you train the you know,', 'start': 1825.445, 'duration': 5.304}], 'summary': 'Ignite library simplifies training loop, reducing errors and complexity in pytorch.', 'duration': 27.455, 'max_score': 1803.294, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1803294.jpg'}], 'start': 1431.405, 'title': 'Neural networks and image recognition', 'summary': 'Discusses achieving 97.5% accuracy with neural networks, emphasizing the need for caution due to potential biases and implementing convolutional neural networks for image recognition using pytorch, including model evaluation and mnist dataset limitations.', 'chapters': [{'end': 1511.262, 'start': 1431.405, 'title': 'Neural networks and accuracy warnings', 'summary': 'Discusses the high accuracy of 97.5% achieved with neural networks, highlighting the potential for unnoticed biases and the caution required when dealing with real-life tasks.', 'duration': 79.857, 'highlights': ["Neural networks can easily lead to high accuracy, but it's crucial to remain cautious as it's also easy to inadvertently introduce biases.", 'High accuracy like 97.5% should be a red flag in most real-life tasks, particularly with complex distributions, as it is challenging to achieve.', 'The cautionary note about the potential biases and challenges in achieving high accuracy in real-life tasks is emphasized throughout the discussion.']}, {'end': 1855.175, 'start': 1511.743, 'title': 'Learning convolutional neural networks', 'summary': "Discusses implementing a convolutional neural network for image recognition using pytorch, including evaluating the model's correctness and the limitations of the mnist dataset, and hints at future topics such as server deployment and additional pytorch libraries.", 'duration': 343.432, 'highlights': ['The chapter discusses the limitations of the MNIST dataset and the challenges of applying the knowledge gained to other use cases, especially with imagery, due to pre-processing and normalization issues.', "It hints at future topics such as server deployment and incorporating a GUI for users to hand-draw digits to test the model's predictions.", 'It mentions the potential transition from recurrent neural networks to convolutional neural networks for sequences of data, as the latter is proving to be more effective.', 'It briefly mentions the library Ignite, which aims to simplify the training loop in PyTorch, and the importance of monitoring validation accuracy and graphing loss over time during training.']}], 'duration': 423.77, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/9j-_dOze4IM/pics/9j-_dOze4IM1431405.jpg', 'highlights': ["Neural networks can easily lead to high accuracy, but it's crucial to remain cautious as it's also easy to inadvertently introduce biases.", 'High accuracy like 97.5% should be a red flag in most real-life tasks, particularly with complex distributions, as it is challenging to achieve.', 'The cautionary note about the potential biases and challenges in achieving high accuracy in real-life tasks is emphasized throughout the discussion.', 'The chapter discusses the limitations of the MNIST dataset and the challenges of applying the knowledge gained to other use cases, especially with imagery, due to pre-processing and normalization issues.', 'It mentions the potential transition from recurrent neural networks to convolutional neural networks for sequences of data, as the latter is proving to be more effective.', 'It briefly mentions the library Ignite, which aims to simplify the training loop in PyTorch, and the importance of monitoring validation accuracy and graphing loss over time during training.']}], 'highlights': ['Covers training a deep learning model to recognize handwritten digits using PyTorch and Python.', 'The optimizer adjusts weights based on gradients to lower the loss slowly over time, determined by the learning rate used.', "The concept of decaying learning rate, involving starting with large steps that gradually become smaller over time to aid in descending the 'mountain' and reaching the desired destination.", 'Importance of zeroing gradients to prevent accumulation and improve training accuracy and efficiency.', 'The process of calculating loss and adjusting weights using optimizer.step is demonstrated, with an emphasis on the goal of observing the decline in loss value during training.', "Neural networks can easily lead to high accuracy, but it's crucial to remain cautious as it's also easy to inadvertently introduce biases."]}