# Transformer Architecture
Transformers are a revolutionary architecture that has transformed natural language processing and computer vision.
## Introduction
Introduced in "Attention Is All You Need" (2017), transformers rely entirely on attention mechanisms, eliminating the need for recurrent or convolutional layers.
## Key Components
### Self-Attention Mechanism
Self-attention allows the model to weigh the importance of different parts of the input:
- **Query, Key, Value**: Three learned representations
- **Attention Scores**: Determine relationships between tokens
- **Parallel Processing**: Unlike RNNs, processes all tokens simultaneously
### Multi-Head Attention
Multiple attention heads capture different types of relationships:
- Each head learns different patterns
- Concatenated and projected to final representation
- Enables rich feature extraction
### Positional Encoding
Since transformers have no inherent notion of sequence order:
- **Sinusoidal Encoding**: Fixed positional patterns
- **Learned Embeddings**: Trainable position representations
## Architecture Components
### Encoder
- Stack of identical layers
- Self-attention + feed-forward networks
- Residual connections and layer normalization
### Decoder
- Similar to encoder with additional cross-attention
- Generates output sequences
- Masked self-attention for autoregressive generation
## Vision Transformers (ViT)
Transformers adapted for images:
- Split images into patches
- Treat patches as sequence tokens
- Apply standard transformer architecture
## Applications
- **Natural Language Processing**: BERT, GPT, T5
- **Computer Vision**: Vision Transformers, DETR
- **Multimodal**: CLIP, DALL-E
- **Speech Recognition**: Whisper
## Advantages
- **Parallelization**: Faster training than RNNs
- **Long-range Dependencies**: Better than CNNs for global context
- **Transfer Learning**: Pre-trained models work across tasks
## Conclusion
Transformers have become the dominant architecture in AI, powering state-of-the-art models across multiple domains.