A clear, step-by-step explanation of the Transformer encoder architecture, covering tokenization, positional encoding, self-attention, feed-forward networks, residual connections, and why multiple encoder blocks are used.

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.