How to use Python's @property decorator for clean getter/setter patterns and backward compatibility.

Fundamentals of Bayesian statistics covering Bayes' theorem, conditional probability, posterior updating, and causality interpretation.

CNN fundamentals: convolution operations, kernel mechanics, multi-dimensional convolutions, and backpropagation.

Deep learning fundamentals: key components (data, model, loss, optimizer) and a brief history from AlexNet to GPT-3.

Introduction to neural networks covering linear regression, softmax classification, activation functions, and why deep layers are preferred.

Neural networks as function approximators: linear models, activation functions, multi-layer perceptrons, universal approximation theorem, and loss functions.