Attention mechanisms prioritize important parts of the input data when making predictions. By assigning weights to input elements, the network focuses on relevant features while ignoring irrelevant ones.
In sequence models like Transformers, attention captures dependencies between words, regardless of their positions. Self-attention, for instance, computes relationships within a sequence, enabling tasks like translation or text summarization.
Applications of attention extend to vision (e.g., image captioning) and speech recognition. Key components, like the query, key, and value in scaled dot-product attention, allow flexible and scalable implementations in various domains.