Embeddings & Representations
The lookup table, the residual stream, contextual vectors, geometry and superposition.
17concepts
177flashcards
126minutes of reading
- 01 Cosine Similarity vs Dot Product Two near-identical looking formulas that answer different questions, one measures direction alone, the other measures direction and magnitude together, and picking the wrong one silently breaks a search or ranking system.
- 02 Embeddings and Semantic Search How dense vectors turn text into a geometry of meaning, and how cosine similarity lets you find related content without keywords.
- 03 Input Embeddings and the Lookup Table How a token id becomes a vector, a single row lookup into a trained matrix, and why that matrix is often the largest single block of parameters a small model spends before any real computation happens.
- 04 word2vec and GloVe The two ideas, predict-a-neighbour and factor-a-co-occurrence-matrix, that first proved dense vectors could capture enough word meaning to support arithmetic on it, years before transformers existed.