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. Watch 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 material online: https://learnpytorch.io
Complete Zero to Mastery course (20 hours more video): https://dbourke.link/ZTMPyTorch

Some sections below have been omitted due to YouTube's timestamping limit.

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 versus 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. Overview
1:03:56 10. How to (and how not to) approach this course
1:09:05 11. Important sources
1:14:28 12. Prepare installation
1:22:08 13. Introduction to tensors
1:35:35 14. Creating tensors
1:54:01 17. Tensor data types
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, average and sum
2:57:48 25. Reform, view and stack
3:11:31 26. Squeeze, loosen and switch
3:23:28 27. Selecting data (indexing)
3:33:01 28. PyTorch and NumPy
3:42:10 29. Reproducibility
3:52:58 30. Access to a GPU
4:04:49 31. Set up device agnostic code

Chapter 1 – PyTorch Workflow
4:17:27 33. Introduction to the PyTorch workflow
4:20:14 34. Prepare installation
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. Discuss important lessons about model building
5:20:09 41. We examine the inside of our model
5:30:01 42. Making predictions with our model
5:41:15 43. Training a model with PyTorch (building intuition)
5:49:31 44. Setting up a loss function and optimization
6:02:24 45. PyTorch training loop intuition
6:40:05 48. Running our training loop from time to time
6:49:31 49. Write test loop code
7:15:53 51. Save/load a model
7:44:28 54. Putting it all together

Chapter 2 – Classification of Neural Networks
8:32:00 60. Introduction to Machine Learning Classification
8:41:42 61. Classification input and output
8:50:50 62. Architecture of a classification neural network
9:09:41 64. Converting 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, optimization 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. Discuss options to improve a model
11:27:52 76. Create a straight line data set
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 multi-class problem
13:24:09 88. Troubleshooting a multi-class model

Chapter 3 – Computer Vision
14:00:48 92. Introduction to Computer Vision
14:12:36 93. Computer vision input and output
14:22:46 94. What is a convolutional neural network?
14:27:49 95. TorchVision
14:37:10 96. Obtaining 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 batch data
16:26:27 105. Running experiments on the GPU
16:30:14 106. Creating a model with nonlinear 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. Breakdown nn.Conv2d/nn.MaxPool2d
18:29:02 118. Training our first CNN
18:44:22 120. Making predictions from random test samples
18:56:01 121. Mapping 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. Download a custom dataset of pizza, steak, and sushi images
20:13:59 129. Becoming one with the data
20:39:11 132. Converting images into tensors
21:16:16 136. Image Creating DataLoaders
21:25:20 137. Creating a custom dataset class (overview)
21:42:29 139. Write a brand new dataset class
22:21:50 142. Converting custom data sets into DataLoaders
22:28:50 143. Data Augmentation
22:43:14 144. Building a basic model
23:11:07 147. Getting a summary of our model with torchinfo
23:17:46 148. Creating training and test loop functions
23:50:59 151. Plotting model 0 loss curves
24:00:02 152. Overfitting and underfitting
24:32:31 155. Plot loss curves of model 1
24:35:53 156. Plot all loss curves
24:46:50 157. Predicting from custom data

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