Understanding and Coding the Self-Attention Mechanism of Large Language Models

Feb 10, 2023 ·

Self-Attention from Scratch is a tutorial by Sebastian Raschka, published on February 28, 2023. The tutorial explains the concept of self-attention and provides an implementation from scratch using Python and NumPy. Self-attention is a powerful technique in the world of deep learning and natural language processing (NLP). It has the potential to augment existing techniques, such as recurrent neural networks (RNNs), and transform them into models that capture contextual relationships among words in a sentence or document.

In the tutorial, Sebastian covers the basics of self-attention, such as what it is, why it is useful, and how it works. He then goes on to explain the mathematical equations behind self-attention, followed by a detailed explanation of the steps involved in implementing it from scratch. After that, he provides a simple example code showing self-attention in action.

The tutorial also introduces some of the key parameters associated with self-attention, such as the number of heads, the amount of memory, and the number of attentions per head. Finally, he concludes with a section on potential applications of self-attention, including question answering and summarization.

Overall, the tutorial offers a great introduction to the concept of self-attention, its underlying mathematics, and how to implement it from scratch. The tutorial also provides several examples to illustrate the concept and help readers get a better understanding. As such, it can be a great resource for those interested in learning more about self-attention and its potential applications in NLP.