Multi-head attention addresses a key limitation of self-attention by enabling Transformers to capture multiple semantic perspectives simultaneously. This article explains the intuition, working mechanism, dimensional flow, and original Transformer implementation of multi-head attention using clear examples and mathematical reasoning.

This post explains self-attention using geometric intuition. By visualizing embeddings, dot products, scaling, and weighted vector sums, we see how contextual embeddings shift based on surrounding words and capture meaning relative to context.

Scaled dot-product attention is a core component of Transformer models, but why do we divide by √dₖ before applying softmax? This article explains the variance growth problem in high-dimensional dot products, the role of scaling in stabilizing softmax, and the mathematical intuition that makes attention training reliable and effective.