Look up any term in AI.

Graph Neural Networks (GNN)

A GNN takes adjacency as data, not as an architectural assumption. Message passing worked by hand, why k layers reach d^k nodes, and why deep GNNs collapse.

Inference

Running a trained model to get an answer. Training is paid once; inference is paid on every request — and it is where most AI money goes.

Positional Encoding (RoPE)

Positional encoding tells a transformer where each token sits in a sequence. Why self-attention needs it, and how rotary embeddings (RoPE) work.

Neural Network

Why a stack of layers does what one cannot: two linear layers collapse into a single matrix, and a hidden layer of two units solves XOR.

Naive Bayes

A probabilistic classifier applying Bayes' theorem under a deliberately false feature-independence assumption — fast, data-light, a strong text baseline.

Residual Connections (Skip Connections)

Residual connections add a block's input back to its output — F(x) + x — the trick that lets gradients flow through deep nets and made transformers trainable.