neural-networks
4 free lessons tagged neural-networks across Robotics, AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Modelling Interaction: From Social Forces to Social Pooling
How the field learned to represent people influencing each other: the physics-inspired force model, the Social LSTM pooling layer that replaced hand-designed rules with learned ones, and the attention and graph architectures that followed.
Features, Directions, and Superposition
Why individual neurons are the wrong unit for understanding a neural network, and what the superposition hypothesis says is really going on inside the activations.
What Neural Language Models Can Actually Compute
The same hierarchy that classifies grammars can classify neural networks. This lesson places recurrent networks and transformers on the Chomsky ladder, explains why a transformer is theoretically weaker than it looks, and shows how formal language theory predicts where today's language models generalize and where they break.
Neural Networks and Backpropagation
Build intuition for how artificial neurons stack into layers, why nonlinear activations are non-negotiable, and how the chain rule turns a forward pass into exact gradients — illustrated with a tiny numpy forward+backward walk-through.