Attention allows models to focus on different parts of the input when producing each part of the output, similar to how humans selectively focus on relevant information.
Why Attention Matters:
• Captures long-range dependencies
• Provides interpretability - see what the model focuses on
• Enables parallel computation
• Forms the foundation of transformers
💡 Key Insight
Attention mechanisms solve the information bottleneck problem in sequence-to-sequence models by allowing direct connections between all input and output positions.
Types of Attention
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Self-Attention
Relates different positions within the same sequence
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Cross-Attention
Relates positions between two different sequences
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Multi-Head Attention
Multiple attention mechanisms in parallel
Remember:
Attention weights tell us which parts of the input are most relevant for each output!