Beginner steps to learn pytorch.
Course by Daniel Bourke, posted by freecodecamp.org
- Course:
- course repo:
excercises/
: https://github.com/mrdbourke/pytorch-deep-learning
- โ 0:01:45 0. Welcome and "what is deep learning?"
- โ 0:07:41 1. Why use machine/deep learning?
- โ 0:11:15 2. The number one rule of ML
- โ 0:16:55 3. Machine learning vs deep learning
- โ 0:23:02 4. Anatomy of neural networks
- โ 0:32:24 5. Different learning paradigms
- โ 0:36:56 6. What can deep learning be used for?
- โ 0:43:18 7. What is/why PyTorch?
- โ 0:53:33 8. What are tensors?
- โ 0:57:52 9. Outline
- โ 1:03:56 10. How to (and how not to) approach this course
- โ 1:09:05 11. Important resources
- โ 1:14:28 12. Getting setup
- โ 1:22:08 13. Introduction to tensors
- โ 1:35:35 14. Creating tensors
- โ 1:54:01 17. Tensor datatypes
- โ 2:03:26 18. Tensor attributes (information about tensors)
- โ 2:11:50 19. Manipulating tensors
- โ 2:17:50 20. Matrix multiplication
- โ 2:48:18 23. Finding the min, max, mean & sum
- โ 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
- 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
- 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 โ non-linearity
- 12:42:32 84. Putting it all together with a multiclass problem
- 13:24:09 88. Troubleshooting a mutli-class model
- 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
- 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