Course Curriculum
9 chapters are available now.
Getting Started
Building a Neural Network
The Structure of Neural Networks
Introduces the fundamental building blocks of neural networks, from individual neurons and activation functions to the layered architecture of feedforward networks.
Digit Classifier: Neural Network Architecture
A hands-on lab to implement the basic structure of a neural network, preparing it to classify handwritten digits.
Training Neural Networks
Explores how neural networks learn using cost functions, gradient descent, and the backpropagation algorithm.
Digit Classifier: Training a Neural Network
This lab applies the concepts of backpropagation and stochastic gradient descent to train the digit classifier network.
Behind the Scenes of Training
Explores the intuition behind the training algorithms and discusses what a neural network actually learns, from simple features to complex patterns.
Interpreting Neural Networks
Introduces mechanistic interpretability as a framework for reverse-engineering what a trained neural network has learned, examining how individual neurons, weights, and activations encode features and combine to produce meaningful behavior.