Chapter 8: RMS Normalisation and Residual Connections
What You'll Build Two architectural patterns that make deep networks trainable: RMSNorm (keeps activations from exploding or vanishing) and residual c…
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Chapter 7: The Training Loop and Adam Optimiser
What You'll Build A complete training loop that processes documents, computes loss, backpropagates gradients, and updates parameters using the Adam op…
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 …
Without google's transformers, there is no GPT-ishs
Yes, remember back there in 2020/2021 when OpenAI created the gpt2? How about we really focus on what enable them to do that? google transformers. The…
Chapter 5: Linear Transformation and Softmax
What You'll Build Two helper functions that show up in nearly every layer of a neural network: Linear takes an input vector and a weight matrix, multi…
Chapter 4: The Bigram Model - Simplest Possible Language Model
What You'll Build A character-level language model that predicts the next character based only on the current character. No neural network, no gradien…
Chapter 3: The Tokenizer - Text to Numbers and Back
What You'll Build A Tokenizer class that converts between characters and integer IDs, plus a special BOS (Beginning of Sequence) token. Depends On Not…