Embeddings

Attention: Turning Token Vectors into Context Vectors The embeddings note ended with an honest limitation: a token’s embedding is one fixed row of a learned matrix. The word “bank” gets the same vector in “river bank” and “investment bank”. Everything the model can possibly know about “bank itself” is frozen into that row at training time. But meaning is contextual. When a model processes a sentence, what it needs at the position of “bank” is not “the generic bank vector” but “the vector of bank as it appears in this sentence”. Attention is the mechanism that builds that second thing from the first. ...

Embeddings: From One-Hot Vectors to Learned Representations Everything in the previous notes assumed the network’s input was already a list of numbers — x1 = 1, x2 = 2, pixel intensities, whatever. But most interesting data is not numeric. “cat”, “dog”, “bank”, user IDs, product IDs, words of a sentence. Neural networks cannot multiply the string "cat" by a weight matrix. Somewhere between the raw symbol and the first linear layer, a translation to numbers must happen, and the way we do it — the embedding layer — turns out to be one of the most consequential ideas in modern deep learning. ...