Reading / Videos

Reading / Videos

This page is intentionally link-heavy: it’s meant to be your “syllabus”.

Classes / OOP / Python data model

• Python docs: Data model (__dunder__ methods): docs.python.org/3/reference/datamodel.html
• Python docs: dataclasses: docs.python.org/3/library/dataclasses.html
• Python docs: typing (Protocols, generics): docs.python.org/3/library/typing.html
• Raymond Hettinger (excellent talks; search the titles):
  • “Transforming Code into Beautiful, Idiomatic Python”
  • “Beyond PEP 8”

NumPy

• NumPy: Quickstart tutorial: numpy.org/doc/stable/user/quickstart.html
• NumPy: Broadcasting: numpy.org/doc/stable/user/basics.broadcasting.html
• NumPy: einsum: numpy.org/doc/stable/reference/generated/numpy.einsum.html
• MIT 18.06 (Linear Algebra) video lectures (good companion for linalg parts): ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/

PyTorch (minimal)

• PyTorch: Tensors: pytorch.org/docs/stable/tensors.html
• PyTorch: Autograd: pytorch.org/docs/stable/autograd.html
• PyTorch: nn.Module: pytorch.org/docs/stable/generated/torch.nn.Module.html

Setup notes

• Recommended: create a fresh env with python -m venv .venv and install numpy and torch.
• If you can’t install torch easily on your machine, you can still do the classes + NumPy parts.