- 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.