DurianDan / PyTorch_24hrs

Learn Applied Machine Learning through PyTorch, course taught by Daniel Bourke

Learn PyTorch in ~25hours

Beginner steps to learn pytorch.
    Course by Daniel Bourke, posted by freecodecamp.org

References:

• Course:
  • Video version
  • Website version
• course repo: excercises/: https://github.com/mrdbourke/pytorch-deep-learning

Course Progress

🛠 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 & 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