Chapter 10: Multi-Head Attention and the MLP Block

1. 1Implement multi-head attention by running several attention heads in parallel on embedding slices
2. 2Add a two-layer MLP for per-position computation
3. 3Assemble a transformer block by combining the multi-head attention and MLP
4. 4Use the transformer block to build a GPT model from scratch
5. 5Test the GPT model on a dataset to evaluate its performance

Machine learning engineers and data scientists can benefit from this tutorial to improve their understanding of transformer architecture and implement it in their projects.

💡 Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions.

🤖 Learn to build a GPT model from scratch with multi-head attention and MLP block! 💻