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ch12

Chapter 12: Parallelizing Neural Network Training with PyTorch

Chapter Outline

  • PyTorch and training performance
    • Performance challenges
    • What is PyTorch?
    • How we will learn PyTorch
  • First steps with PyTorch
    • Installing PyTorch
    • Creating tensors in PyTorch
    • Manipulating the data type and shape of a tensor
    • Applying mathematical operations to tensors
    • Split, stack, and concatenate tensors
  • Building input pipelines in PyTorch
    • Creating a PyTorch DataLoader from existing tensors
    • Combining two tensors into a joint dataset
    • Shuffle, batch, and repeat
    • Creating a dataset from files on your local storage disk
    • Fetching available datasets from the torchvision.datasets library
  • Building an NN model in PyTorch
    • The PyTorch neural network module (torch.nn)
    • Building a linear regression model
    • Model training via the torch.nn and torch.optim modules
    • Building a multilayer perceptron for classifying flowers in the Iris dataset
    • Evaluating the trained model on the test dataset
    • Saving and reloading the trained model
  • Choosing activation functions for multilayer neural networks
    • Logistic function recap
    • Estimating class probabilities in multiclass classification via the softmax function
    • Broadening the output spectrum using a hyperbolic tangent
    • Rectified linear unit activation
  • Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.