Multi-Head Attention — Many Sets of Eyes, Explained | datarekha

One attention pass learns one kind of relationship, but language has many at once. Run attention many times in parallel — each head with its own query/key/value matrices, projecting into a different subspace to look for a different thing (grammar, meaning, long-range links). Concatenate every head's blend and mix with one learned matrix. That diversity is much of why transformers are so powerful. Chapter 61 of the full "ML & DL from scratch, with the math" course (watch the complete ~2h09m film, with all chapters & timestamps in its pinned comment). More at datarekha.com. Narration uses a synthetic AI voice.

Related free lessons on datarekha.com:
- Multi-head attention: https://datarekha.com/deep-learning/multi-head

Видео Multi-Head Attention — Many Sets of Eyes, Explained | datarekha канала datarekha