“Cosine Similarity” is everywhere in machine learning, but it’s often treated as a black box. At its core, it’s just a normalized measure in a vector space, comparing two document representations. It doesn’t really understand meaning, it’s a purely geometric view based on the angle between vectors, yet it works surprisingly well at capturing how similar two documents are, almost as if it understood their content. Simple idea, but the intuition behind it is genuinely elegant. This is one of the most read pages on Algebrica. algebrica.org/cosine-similar…
→ View original post on X — @deeplearn007, 2026-04-05 13:22 UTC
