Chapter 13: Going Deeper – The Mechanics of PyTorch

Chapter Outline

• 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.