Pytorch For Deep Learning Machine Learning Full Course

Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.. . ✏️ Daniel Bourke developed this course. Check out his channel: https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ. . 🔗 Code: https://github.com/mrdbourke/pytorch-deep-learning. 🔗 Ask a question: https://github.com/mrdbourke/pytorch-deep-learning/discussions. 🔗 Course materials online: https://learnpytorch.io. 🔗 Full course on Zero to Mastery (20+ hours more video): https://dbourke.link/ZTMPyTorch. . Some sections below have been left out because of the YouTube limit for timestamps.. . 0:00:00 Introduction. . 🛠 Chapter 0 – PyTorch Fundamentals. 0:01:45 0. Welcome and “what is deep learning?”. 0:07:41 1. Why use machine/deep learning?. 0:11:15 2. The number one rule of ML. 0:16:55 3. Machine learning vs deep learning. 0:23:02 4. Anatomy of neural networks. 0:32:24 5. Different learning paradigms. 0:36:56 6. What can deep learning be used for?. 0:43:18 7. What is/why PyTorch?. 0:53:33 8. What are tensors?. 0:57:52 9. Outline. 1:03:56 10. How to (and how not to) approach this course. 1:09:05 11. Important resources. 1:14:28 12. Getting setup. 1:22:08 13. Introduction to tensors. 1:35:35 14. Creating tensors. 1:54:01 17. Tensor datatypes. 2:03:26 18. Tensor attributes (information about tensors). 2:11:50 19. Manipulating tensors. 2:17:50 20. Matrix multiplication. 2:48:18 23. Finding the min, max, mean u0026 sum. 2:57:48 25. Reshaping, viewing and stacking. 3:11:31 26. Squeezing, unsqueezing and permuting. 3:23:28 27. Selecting data (indexing). 3:33:01 28. PyTorch and NumPy. 3:42:10 29. Reproducibility. 3:52:58 30. Accessing a GPU. 4:04:49 31. Setting up device agnostic code. . 🗺 Chapter 1 – PyTorch Workflow. 4:17:27 33. Introduction to PyTorch Workflow. 4:20:14 34. Getting setup. 4:27:30 35. Creating a dataset with linear regression. 4:37:12 36. Creating training and test sets (the most important concept in ML). 4:53:18 38. Creating our first PyTorch model. 5:13:41 40. Discussing important model building classes. 5:20:09 41. Checking out the internals of our model. 5:30:01 42. Making predictions with our model. 5:41:15 43. Training a model with PyTorch (intuition building). 5:49:31 44. Setting up a loss function and optimizer. 6:02:24 45. PyTorch training loop intuition. 6:40:05 48. Running our training loop epoch by epoch. 6:49:31 49. Writing testing loop code. 7:15:53 51. Saving/loading a model. 7:44:28 54. Putting everything together. . 🤨 Chapter 2 – Neural Network Classification. 8:32:00 60. Introduction to machine learning classification. 8:41:42 61. Classification input and outputs. 8:50:50 62. Architecture of a classification neural network. 9:09:41 64. Turing our data into tensors. 9:25:58 66. Coding a neural network for classification data. 9:43:55 68. Using torch.nn.Sequential. 9:57:13 69. Loss, optimizer and evaluation functions for classification. 10:12:05 70. From model logits to prediction probabilities to prediction labels. 10:28:13 71. Train and test loops. 10:57:55 73. Discussing options to improve a model. 11:27:52 76. Creating a straight line dataset. 11:46:02 78. Evaluating our model’s predictions. 11:51:26 79. The missing piece – non-linearity. 12:42:32 84. Putting it all together with a multiclass problem. 13:24:09 88. Troubleshooting a mutli-class model. . 😎 Chapter 3 – Computer Vision. 14:00:48 92. Introduction to computer vision. 14:12:36 93. Computer vision input and outputs. 14:22:46 94. What is a convolutional neural network?. 14:27:49 95. TorchVision. 14:37:10 96. Getting a computer vision dataset. 15:01:34 98. Mini-batches. 15:08:52 99. Creating DataLoaders. 15:52:01 103. Training and testing loops for batched data. 16:26:27 105. Running experiments on the GPU. 16:30:14 106. Creating a model with non-linear functions. 16:42:23 108. Creating a train/test loop. 17:13:32 112. Convolutional neural networks (overview). 17:21:57 113. Coding a CNN. 17:41:46 114. Breaking down nn.Conv2d/nn.MaxPool2d. 18:29:02 118. Training our first CNN. 18:44:22 120. Making predictions on random test samples. 18:56:01 121. Plotting our best model predictions. 19:19:34 123. Evaluating model predictions with a confusion matrix. . 🗃 Chapter 4 – Custom Datasets. 19:44:05 126. Introduction to custom datasets. 19:59:54 128. Downloading a custom dataset of pizza, steak and sushi images. 20:13:59 129. Becoming one with the data. 20:39:11 132. Turning images into tensors. 21:16:16 136. Creating image DataLoaders. 21:25:20 137. Creating a custom dataset class (overview). 21:42:29 139. Writing a custom dataset class from scratch. 22:21:50 142. Turning custom datasets into DataLoaders. 22:28:50 143. Data augmentation. 22:43:14 144. Building a baseline model. 23:11:07 147. Getting a summary of our model with torchinfo. 23:17:46 148. Creating training and testing loop functions. 23:50:59 151. Plotting model 0 loss curves. 24:00:02 152. Overfitting and underfitting. 24:32:31 155. Plotting model 1 loss curves. 24:35:53 156. Plotting all the loss curves. 24:46:50 157. Predicting on custom data