Build tiny models fast, minimize loss on FineWeb under limits.
Build tiny models fast, minimize loss on FineWeb under limits.
Case differences break word counts, so lowercase everything first always.
Embeddings just pick a row, no extra math needed.
Computer vision is math, but frameworks like PyTorch make it easier.
Short term memory is recent words, long term keeps earlier context.
We need get, set, and delete, even if it looks weird.
Python stays my favorite for clean, concise machine learning code.
Rust teaches hard lessons, then changes the rules on you.