Learn PyTorch for deep learning and machine learning. This full course covers tensors, neural networks, model building, and computer vision.

• Introduction
• 0. Welcome and "what is deep learning?"
• 1. Why use machine/deep learning?
• 2. The number one rule of ML
• 3. Machine learning vs deep learning
• 4. Anatomy of neural networks
• 5. Different learning paradigms
• 6. What can deep learning be used for?
• 7. What is/why PyTorch?
• 8. What are tensors?
• 9. Outline
• 10. How to (and how not to) approach this course
• 11. Important resources
• 12. Getting setup
• 13. Introduction to tensors
• 14. Creating tensors
• 17. Tensor datatypes
• 18. Tensor attributes (information about tensors)
• 19. Manipulating tensors
• 20. Matrix multiplication
• 23. Finding the min, max, mean & sum
• 25. Reshaping, viewing and stacking
• 26. Squeezing, unsqueezing and permuting
• 27. Selecting data (indexing)
• 28. PyTorch and NumPy
• 29. Reproducibility
• 30. Accessing a GPU
• 31. Setting up device agnostic code
• 33. Introduction to PyTorch Workflow
• 34. Getting setup
• 35. Creating a dataset with linear regression
• 36. Creating training and test sets (the most important concept in ML)
• 38. Creating our first PyTorch model
• 40. Discussing important model building classes
• 41. Checking out the internals of our model
• 42. Making predictions with our model
• 43. Training a model with PyTorch (intuition building)
• 44. Setting up a loss function and optimizer
• 45. PyTorch training loop intuition
• 48. Running our training loop epoch by epoch
• 49. Writing testing loop code
• 51. Saving/loading a model
• 54. Putting everything together
• 60. Introduction to machine learning classification
• 61. Classification input and outputs
• 62. Architecture of a classification neural network
• 64. Turing our data into tensors
• 66. Coding a neural network for classification data
• 68. Using torch.nn.Sequential
• 69. Loss, optimizer and evaluation functions for classification
• 70. From model logits to prediction probabilities to prediction labels
• 71. Train and test loops
• 73. Discussing options to improve a model
• 76. Creating a straight line dataset
• 78. Evaluating our model's predictions
• 79. The missing piece – non-linearity
• 84. Putting it all together with a multiclass problem
• 88. Troubleshooting a mutli-class model
• 92. Introduction to computer vision
• 93. Computer vision input and outputs
• 94. What is a convolutional neural network?
• 95. TorchVision
• 96. Getting a computer vision dataset
• 98. Mini-batches
• 99. Creating DataLoaders
• 103. Training and testing loops for batched data
• 105. Running experiments on the GPU
• 106. Creating a model with non-linear functions
• 108. Creating a train/test loop
• 112. Convolutional neural networks (overview)
• 113. Coding a CNN
• 114. Breaking down nn.Conv2d/nn.MaxPool2d
• 118. Training our first CNN
• 120. Making predictions on random test samples
• 121. Plotting our best model predictions
• 123. Evaluating model predictions with a confusion matrix
• 126. Introduction to custom datasets
• 128. Downloading a custom dataset of pizza, steak and sushi images
• 129. Becoming one with the data
• 132. Turning images into tensors
• 136. Creating image DataLoaders
• 137. Creating a custom dataset class (overview)
• 139. Writing a custom dataset class from scratch
• 142. Turning custom datasets into DataLoaders
• 143. Data augmentation
• 144. Building a baseline model
• 147. Getting a summary of our model with torchinfo
• 148. Creating training and testing loop functions
• 151. Plotting model 0 loss curves
• 152. Overfitting and underfitting
• 155. Plotting model 1 loss curves
• 156. Plotting all the loss curves
• 157. Predicting on custom data