Look up any term in AI.

Naive Bayes

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

Monitoring

Why a deployed model rots while every dashboard stays green — and what you can actually measure when the true labels arrive weeks late, or never.

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.

Recurrent Neural Network (RNN)

A recurrent neural network reads a sequence one element at a time, carrying a hidden state that summarizes everything seen so far to model order and context.

Active Learning

Active learning lets a model choose which unlabelled examples to get labelled next, hitting target accuracy with far fewer labels than random labelling.

Convolution

Convolution slides a small kernel over an image, multiplying and summing each patch into one output number. The arithmetic of stride, padding and kernels.