Multi-head

Multi-head, often used in the context of machine learning and neural networks, refers to a mechanism where multiple heads or attention mechanisms are employed within a model, such as in a multi-head attention layer in transformer models. This allows the model to focus on different parts of the input data simultaneously, enhancing its ability to capture various relationships and dependencies within the data. Multi-head mechanisms are particularly beneficial for natural language processing tasks and complex data analysis, making them popular among AI researchers and developers seeking to improve model performance and accuracy.