Transformers and Multi-Head Attention, Mathematically Explained

A transformer neural network model is a type of neural network architecture that has become the foundation for many modern natural language processing (NLP) tasks, such as chatbots, translation, and text generation.

The main innovation behind the transformer is the self-attention mechanism—originally created for encoder–decoder RNNs for machine translation.

The attention mechanism allows them to weigh the importance of different tokens (words or symbols) in an input sequence when making predictions.

Before continuing, it is recommended to understand how transformers work conceptually, as this issue is long and includes a worked example with math. We have an issue introducing transformers below:

• Introduction to Transformers and the Attention Mechanism (Intermediate)