Understanding Query, Key and Value Vectors in Transformer Networks

This video provides an explanation of query, key and value vectors, which are an essential part of the attention mechanism used in transformer neural networks. Transformers use multi-headed attention to learn contextual relationships between input and output sequences. The attention mechanism calculates the relevance of one element to another based on query and key vectors. The value vectors then provide contextual information for the relevant elements. Understanding how query, key and value vectors work can help in designing and optimizing transformer models for various natural language processing and computer vision tasks.