Multi-Head Attention
Running multiple attention operations in parallel with different learned projections, capturing diverse relational patterns.
Related Concepts
- Self-Attention: Explore how Self-Attention relates to Multi-Head Attention
- Transformer: Explore how Transformer relates to Multi-Head Attention
- Attention Head: Explore how Attention Head relates to Multi-Head Attention
Why It Matters
Understanding Multi-Head Attention is crucial for anyone working with large language models. This concept helps build a foundation for more advanced topics in AI and machine learning.
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This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
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Related Terms
Attention Head
An individual attention mechanism in multi-head attention, learning specific patterns of relationships between tokens.
Self-Attention
A mechanism where each token attends to all other tokens in the sequence to understand contextual relationships.
Transformer
A neural network architecture introduced in 'Attention is All You Need' (2017) that relies entirely on self-attention mechanisms, becoming the foundation for modern LLMs.