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

Introduces the fundamental building blocks of neural networks, from individual neurons and activation functions to the layered architecture of feedforward networks.

A hands-on lab to implement the basic structure of a neural network, preparing it to classify handwritten digits.

Explores how neural networks learn using cost functions, gradient descent, and the backpropagation algorithm.

This lab applies the concepts of backpropagation and stochastic gradient descent to train the digit classifier network.

Explores the intuition behind the training algorithms and discusses what a neural network actually learns, from simple features to complex patterns.

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