In this article, the author discusses self-attention mechanisms used in transformer architectures and large language models (LLMs). They believe that understanding and coding algorithms from scratch is an excellent way to learn. The article focuses on the self-attention mechanism and provides a Python and PyTorch code walkthrough to help readers understand how it works. The author also mentions that self-attention has become a cornerstone of many state-of-the-art deep learning models in natural language processing. They provide code examples for computing attention weights and context vectors using the self-attention mechanism. Additionally, they explain the concept of multi-head attention in the context of self-attention. The article is a modernized and extended version of a previous article written by the author, and it serves as a preview of their upcoming book on building large language models from scratch.
https://magazine.sebastianraschka.com/p/understanding-and-coding-self-attention