similarity

Dense embeddings help when lexical overlap is weak but semantic similarity matters. I use them for retrieval prototypes, clustering, and feature enrichment when transformer infrastructure is overkill. The main discipline is keeping training data clean

A lot of machine learning reduces to linear algebra with better tooling. Dot products, norms, matrix multiplication, and projections show up in recommendation, embeddings, PCA, and optimization. I keep the implementation small and testable so it stays