Attention Mechanism
The attention mechanism helps models focus on important parts of the input. It gives each token a weight. Higher weights mean more importance. This improves understanding of long sequences.
Why Attention Matters
- Captures long range relations
- Handles complex patterns
- Improves context understanding
- Works for text, images, and audio
Core Idea
Each token checks other tokens and decides how much to focus on them. The model uses queries, keys, and values to score relevance.
Components
- Query. What the token needs.
- Key. What the token offers.
- Value. Information passed to the next layer.
How Attention Works
- Compute similarity between query and all keys.
- Convert scores into weights with softmax.
- Multiply weights with values.
- Sum the results to produce the final output.
Scaled Dot Product Attention
This is the standard attention in transformer models. It uses dot product between queries and keys and scales the result. The scaling stabilizes training.
Multi Head Attention
The model splits attention into multiple heads. Each head learns a different relationship. The outputs of all heads merge into one vector. This improves representation strength.
Types of Attention
1. Self Attention
Each token attends to itself and to all other tokens. This helps the model learn context inside the sequence.
2. Cross Attention
Used in encoder decoder models. The decoder attends to encoder outputs. This helps tasks like translation.
3. Local Attention
Attention applied to nearby tokens only. This reduces compute cost.
4. Global Attention
Some tokens attend across the entire sequence. Useful for long input models.
Applications of Attention
- Machine translation
- Text summarization
- Question answering
- Image captioning
- Speech tasks
Strengths
- Strong understanding of context
- Parallel computation
- Flexible for different data types
Limitations
- High memory use
- Heavy compute needs for long sequences
Attention Mechanism in Moroccan Darija
Attention hiyya tariqa kats3ed model ychouf l parts l mohimmin f input. Kul token kay7seb relevance dial tokens okhrin w kay3ti weights.
Kif Kaykhddam
- Query bach n3rf shno bghina.
- Key bach n3rf shno kay3ti token.
- Value bach n9addo info.
- Scores kayt7awlo weights b softmax.
- Weights kaytjma3o m3a values.
Types
- Self attention.
- Cross attention.
- Local.
- Global.
Conclusion
Attention helps deep learning models focus on useful information. It supports strong performance in transformers and drives progress in modern AI.