Understanding Attention in Neural Networks
The attention mechanism is a technique that enables neural networks to selectively focus on specific parts of an input sequence when generating each element of an output. Rather than compressing an entire input into a single fixed-size vector, attention computes a weighted combination of all input elements, with weights determined by their relevance to the current task.
Multi-Head Attention
Self-attention, the variant used in Transformers, computes relationships between all positions within a single sequence. Each token generates three vectors: a query, a key, and a value. The dot product between queries and keys determines how much attention each token pays to every other token, and these weights are used to create context-aware representations.
Enterprise Applications
Multi-head attention runs several attention operations in parallel, each with different learned projections. This allows the model to capture different types of relationships simultaneously, such as syntactic structure in one head and semantic meaning in another. The outputs are concatenated and projected to produce the final result.