Attention in transformers, step-by-step | Deep Learning Chapter 6

Summary by Krater

This video explains the attention mechanism in transformer models, covering queries, keys, values, dot products, softmax normalization, masking, and multi-headed attention in detail.

From the video

Answers: How does the attention mechanism work in transformers?

• transformer attention mechanism
• query, key, and value vectors
• dot product attention
• softmax normalization
• causal masking
• multi-headed attention

What it concludes

• The attention mechanism allows transformer models to move information between token embeddings, encoding rich contextual meaning.
• Dot products between query and key vectors measure how relevant each word is to updating another word's embedding.
• Softmax normalization scales dot product scores to sum to one, acting as weights for the value vectors.
• Causal masking sets future token scores to negative infinity before softmax to prevent later tokens from influencing earlier ones.
• Multi-headed attention runs multiple attention operations in parallel, allowing the model to learn diverse ways context changes meaning.