Python

Each lecture has:

• topic + a comprehensive YouTube tutorial
• extra readings
• homework

Syllabus

Lecture 1 — Classes & Python’s data model

Topic: object design, invariants, dataclasses, protocols, iterators, context managers

• Page: /kicsigeze/classes/
• Notebook: /kicsigeze/notebooks/01_classes_oop.ipynb
• Video: YouTube (search): Python OOP / data model / dunder methods

Lecture 2 — NumPy foundations

Topic: vectorization, broadcasting, indexing, einsum, numerical stability

• Page: /kicsigeze/numpy/
• Notebook: /kicsigeze/notebooks/02_numpy_foundations.ipynb
• Video: YouTube (search): NumPy full course

Lecture 3 — PyTorch mini-intro

Topic: tensors, autograd, minimal training loop

• Page: /kicsigeze/pytorch/
• Notebook: /kicsigeze/notebooks/03_pytorch_mini_intro.ipynb
• Video: YouTube (search): PyTorch full course autograd training loop

Course bibliography

See: Reading / Videos