Multi-Head Attention

Multi-Head Attention is a technique used in AI models to help understand complex information, like language. It works by looking at the data from multiple perspectives simultaneously, focusing on different parts of the input at once. Imagine trying to understand a story; instead of just listening to one part, your brain considers several details, like who is involved, what happened, and why. By doing this in parallel, the model gains a richer, more nuanced understanding of the data. This process improves tasks like translation, summarization, and language understanding, making machines better at interpreting human language.