Lab 2.1: How Word Vectors Learn to Mean — Embeddings From Zero, and What They Secretly Compute
Lab 2.1: How Word Vectors Learn to Mean — Embeddings From Zero, and What They Secretly Compute Language-model internals series. In the MNIST series we took apart what weights represent for vision. New series, same ritual — build it from scratch, check gradients numerically, claim nothing without a baseline or a measured number. The question for this lab, in the plainest possible form: A word becomes a row of numbers. How can those numbers possibly mean anything? ...