👉 Attention math is a mathematical framework that enables models, particularly in sequence-to-sequence tasks like machine translation or text summarization, to focus on relevant parts of input sequences when generating outputs. It quantifies the importance of different elements in the input based on learned weights, often using mechanisms like dot-product attention or scaled dot-product attention. These weights are computed by taking the dot product of query vectors (representing the current context or task) and key vectors (representing input elements), scaled by a learning rate, and then normalized. This process allows the model to dynamically allocate more "attention" to certain parts of the input, enhancing its ability to capture dependencies and context, leading to more accurate and contextually relevant outputs.
