Lab Notebooks:
Homework 1: Homework1.pdf
For self-study (all students):
- Essence of linear algebra (linear transformations; matrix multiplication)
- Essence of calculus (derivatives; chain rule)
- Neural Networks (chapter 1 - chapter 4) (animated introduction to neural networks and backpropagation)
- Backpropagation example, from scratch
- https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html
Advanced (for students who want to learn more):
- Advanced Benchmarking in PyTorch: https://pytorch.org/tutorials/recipes/recipes/benchmark.html
- TorchScript (PyTorch jit): https://pytorch.org/docs/stable/jit.html
- PyTorch jit trace: https://pytorch.org/docs/stable/generated/torch.jit.trace.html
- PyTorch jit script: https://pytorch.org/docs/stable/generated/torch.jit.script.html#torch.jit.script
- PyTorch compile: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html
torch.compiledoes not work on Windows systems!
- PyTorch compile deep dive: https://pytorch.org/docs/stable/torch.compiler_dynamo_deepdive.html
References:
- Tensor creation: https://pytorch.org/docs/stable/torch.html#creation-ops
- Tensor data types: https://pytorch.org/docs/stable/tensors.html#data-types
- Tensor math operations: https://pytorch.org/docs/stable/torch.html#math-operations