Chapter 6: Embeddings, the Forward Pass, and the Loss Function

What You'll Build Embedding tables that give each token and each position a learned vector, a minimal forward pass that produces logits, and the loss function that measures how wrong the predictions are. Depends On Chapters 1-3, 5 (Value, Tokenizer, Helpers). Embeddings: Giving Tokens an Identity The model needs two pieces of information about each token: what the token is, and where it appears in the sequence. Each piece gets its own embedding. We'll start with the first one (token embeddings)