Introduces the course structure, learning objectives, and the prerequisites needed to get started.

Explains why backpropagation is necessary and how it works conceptually before any implementation.

Walks through building a minimal backpropagation engine from scratch, implementing the core backward pass logic that enables automatic differentiation.

Validates the backpropagation engine by running it against known gradient computations, then uses it to train a small neural network end-to-end.

Explains the topological sort algorithm and why it is essential for ordering nodes correctly during the backward pass through a computation graph.

A comprehensive list of credits for the sources that inspired and informed the course material. Useful for further reading.