Self-Attention

2 items

DOCDEV.to AI·4/17/2026

Understanding Transformers Part 9: Stacking Self-Attention Layers

This article explains why self-attention values replace original positional encodings, as they integrate contextual information from all words, clarifying relationships. It then introduces stacking multiple self-attention layers, each with unique weights, to capture more complex linguistic relationships within sentences and paragraphs.

neural networks Self-Attention deep learning NLP

DOCDEV.to AI·4/16/2026

Understanding Transformers Part 8: Shared Weights in Self-Attention

The article explains that Transformers reuse the same set of weights for queries, keys, and values across all input words, enabling parallel computation. This reusability makes the self-attention mechanism highly efficient.

neural networks Self-Attention deep learning Parallel Computing