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ch13

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.