Term tag: representation learning

Autoencoder

A neural network trained to reproduce its own input through a bottleneck too narrow to carry it — so the compressed code, not the copy, is the product.

Deep Learning

What makes deep learning deep: each layer builds on the one below, so capacity grows multiplicatively with depth and only additively with width.

Embedding

What an embedding vector actually is, why cosine similarity beats Euclidean distance, what dimensions buy you, and where the geometry misleads you.

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.

Representation Learning

Representation learning is machine learning that discovers the useful features of raw data automatically, instead of humans hand-engineering them.

Self-supervised Learning (SSL)

Training a model on unlabeled data by hiding part of each input and making the model predict it — the paradigm behind next-token prediction and modern LLMs.