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nn Basics
Joel Schlosser edited this page Jul 1, 2021
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- Understand what
torch.nnis - Understand what a module is
- Understand how modules are used to build and train neural networks
- Understand how to author a module in PyTorch
- Understand how to test modules in PyTorch
torch.nn is the component of PyTorch that provides building blocks for neural networks. Its core abstraction is nn.Module, which encapsulates stateful computation with learnable parameters. Modules integrate with the autograd system and are generally trained using optimizers provided in torch.optim.
Read through the following links:
Work through the lab.
PyTorch presented to you with love by the PyTorch Team of contributors
- Install Prerequisites and Dependencies
- Fork, clone, and checkout the PyTorch source
- Build PyTorch from source
- Tips for developing PyTorch
- PyTorch Workflow Git cheatsheet
- Overview of the Pull Request Lifecycle
- Finding Or Creating Issues
- Pre Commit Checks
- Create a Pull Request
- Typical Pull Request Workflow
- Pull Request FAQs
- Getting Help
- Codebase structure
- Tensors, Operators, and Testing
- Autograd
- Dispatcher, Structured Kernels, and Codegen
- torch.nn
- CUDA basics
- Data (Optional)
- function transforms (Optional)