Watch Learn PyTorch for deep learning in a day. Literally. Video Tutorial

Welcome to the most beginner-friendly place on the internet to learn PyTorch for deep learning. All code on GitHub - https://dbourke.link/pt-github Ask a question - https://dbourke.link/pt-github-discussions Read the course materials online - https://learnpytorch.io Sign up for the full course on Zero to Mastery (20+ hours more video) - https://dbourke.link/ZTMPyTorch Below are the timestamps/outline of the video. The video you're watching is comprised of 162 smaller videos but YouTube limits timestamps at 100 so some have been left out. 00:00 Hello :) 🛠 Chapter 0: PyTorch Fundamentals 01:17 0. Welcome and "what is deep learning?" 07:13 1. Why use machine/deep learning? 10:47 2. The number one rule of ML 16:27 3. Machine learning vs deep learning 22:34 4. Anatomy of neural networks 31:56 5. Different learning paradigms 36:28 6. What can deep learning be used for? 42:50 7. What is/why PyTorch? 53:05 8. What are tensors? 57:24 9. Outline 1:03:28 10. How to (and how not to) approach this course 1:08:37 11. Important resources 1:14:00 12. Getting setup 1:21:40 13. Introduction to tensors 1:35:07 14. Creating tensors 1:53:33 17. Tensor datatypes 2:02:58 18. Tensor attributes (information about tensors) 2:11:22 19. Manipulating tensors 2:17:22 20. Matrix multiplication 2:47:50 23. Finding the min, max, mean and sum 2:57:20 25. Reshaping, viewing and stacking 3:11:03 26. Squeezing, unsqueezing and permuting 3:23:00 27. Selecting data (indexing) 3:32:33 28. PyTorch and NumPy 3:41:42 29. Reproducibility 3:52:30 30. Accessing a GPU 4:04:21 31. Setting up device agnostic code 🗺 Chapter 1: PyTorch Workflow 4:16:59 33. Introduction to PyTorch Workflow 4:19:46 34. Getting setup 4:27:02 35. Creating a dataset with linear regression 4:36:44 36. Creating training and test sets (the most important concept in ML) 4:52:50 38. Creating our first PyTorch model 5:13:13 40. Discussing important model building classes 5:19:41 41. Checking out the internals of our model 5:29:33 42. Making predictions with our model 5:40:47 43. Training a model with PyTorch (intuition building) 5:49:03 44. Setting up a loss function and optimizer 6:01:56 45. PyTorch training loop intuition 6:39:37 48. Running our training loop epoch by epoch 6:49:03 49. Writing testing loop code 7:15:25 51. Saving/loading a model 7:44:00 54. Putting everything together 🤨 Chapter 2: Neural Network Classification 8:31:32 60. Introduction to machine learning classification 8:41:14 61. Classification input and outputs 8:50:22 62. Architecture of a classification neural network 9:09:13 64. Turing our data into tensors 9:25:30 66. Coding a neural network for classification data 9:43:27 68. Using torch.nn.Sequential 9:56:45 69. Loss, optimizer and evaluation functions for classification 10:11:37 70. From model logits to prediction probabilities to prediction labels 10:27:45 71. Train and test loops 10:57:27 73. Discussing options to improve a model 11:27:24 76. Creating a straight line dataset 11:45:34 78. Evaluating our model's predictions 11:50:58 79. The missing piece: non-linearity 12:42:04 84. Putting it all together with a multiclass problem 13:23:41 88. Troubleshooting a mutli-class model 😎 Chapter 3: Computer Vision 14:00:20 92. Introduction to computer vision 14:12:08 93. Computer vision input and outputs 14:22:18 94. What is a convolutional neural network? 14:27:21 95. TorchVision 14:36:42 96. Getting a computer vision dataset 15:01:06 98. Mini-batches 15:08:24 99. Creating DataLoaders 15:51:33 103. Training and testing loops for batched data 16:25:59 105. Running experiments on the GPU 16:29:46 106. Creating a model with non-linear functions 16:41:55 108. Creating a train/test loop 17:13:04 112. Convolutional neural networks (overview) 17:21:29 113. Coding a CNN 17:41:18 114. Breaking down nn.Conv2d/nn.MaxPool2d 18:28:34 118. Training our first CNN 18:43:54 120. Making predictions on random test samples 18:55:33 121. Plotting our best model predictions 19:19:06 123. Evaluating model predictions with a confusion matrix 🗃 Chapter 4: Custom Datasets 19:43:37 126. Introduction to custom datasets 19:59:26 128. Downloading a custom dataset of pizza, steak and sushi images 20:13:31 129. Becoming one with the data 20:38:43 132. Turning images into tensors 21:15:48 136. Creating image DataLoaders 21:24:52 137. Creating a custom dataset class (overview) 21:42:01 139. Writing a custom dataset class from scratch 22:21:22 142. Turning custom datasets into DataLoaders 22:28:22 143. Data augmentation 22:42:46 144. Building a baseline model 23:10:39 147. Getting a summary of our model with torchinfo 23:17:18 148. Creating training and testing loop functions 23:50:31 151. Plotting model 0 loss curves 23:59:34 152. Overfitting and underfitting 24:32:03 155. Plotting model 1 loss curves 24:35:25 156. Plotting all the loss curves 24:46:22 157. Predicting on custom data #pytorch #machinelearning #deeplearning