Multi-Head Attention
Last updated
What is Multi-Head Attention?
Multi-head attention is an attention mechanism that runs through the attention process multiple times independently. Each of these independent attention mechanisms is called a "head." The outputs of all the heads are then concatenated and linearly transformed to produce the final output. This allows the model to attend to different parts of the input sequence with different learned representations, capturing a richer set of relationships than a single attention mechanism could.
Free resources to learn Multi-Head Attention
More in stage 34: Attention Mechanisms
Multi-Head Attention: common questions
Can I learn Multi-Head Attention in Hindi?
This lesson is written in English. Once you sign in, open it in the interactive course and ask the AI tutor to explain Multi-Head Attention in Hindi or Hinglish. The tutor answers from this lesson’s own text, so it stays on the syllabus, and it runs on your own Google, OpenAI or Anthropic key.
Is this Multi-Head Attention tutorial for beginners?
It is lesson 134 of 150 in the free Machine Learning roadmap, in stage 34, Attention Mechanisms. If you are new to Machine Learning, start from stage 1 and come back here in order; if you already know the earlier stages, this lesson stands on its own.