From raw text to a working GPT. You'll implement every component of a transformer-based language model from the ground up.
The big picture: how Large Language Models actually work.
How computers represent language (Unicode & UTF-8).
How BPE compresses text into efficient tokens.
How to represent tokens as vectors to capture semantic meaning.
How to encode sequence order into token embeddings.
How attention lets tokens communicate using Query, Key, and Value.
The limitation of single-head attention and how multiple heads overcome it.
How the feed-forward network transforms each token independently.
How residual connections and layer normalization enable deep networks.
How all components combine into the repeatable transformer block.