- The key features of
- PyTorch's computation graphs
- Understanding computation graphs
- Creating a graph in PyTorch
- PyTorch tensor objects for storing and updating model parameters
- Computing gradients via automatic differentiation
- Computing the gradients of the loss with respect to trainable variables
- Understanding automatic differentiation
- Adversarial examples
- Simplifying implementations of common architectures via the torch.nn module
- Implementing models based on nn.Sequential
- Choosing a loss function
- Solving an XOR classification problem
- Making model building more flexible with nn.Module
- Writing custom layers in PyTorch
- Project one - predicting the fuel efficiency of a car
- Working with feature columns
- Training a DNN regression model
- Project two - classifying MNIST handwritten digits
- Higher-level PyTorch APIs: a short introduction to PyTorch Lightning
- Setting up the PyTorch Lightning model
- Setting up the data loaders for Lightning
- Training the model using the PyTorch Lightning Trainer class
- Evaluating the model using TensorBoard
- Summary
Please refer to the README.md file in ../ch01 for more information about running the code examples.