A **transformer** is a neural network architecture that has revolutionized natural language processing and many other areas of machine learning since its introduction in 2017.

## Key Concepts

### Core Innovation: Attention Mechanism
The transformer's breakthrough is the **self-attention** mechanism, which allows the model to process all positions in a sequence simultaneously and determine which parts are most relevant to each other. Think of it like reading a sentence where you can instantly see how each word relates to every other word, rather than reading left-to-right.

### Architecture Components

**Encoder-Decoder Structure** (though many models use just one):
- **Encoder**: Processes the input sequence and creates representations
- **Decoder**: Generates the output sequence using the encoder's representations

**Key Building Blocks**:
- Multi-head attention layers
- Feed-forward neural networks  
- Layer normalization
- Positional encodings (to understand word order)

## Major Advantages

1. **Parallelization**: Unlike RNNs, transformers process entire sequences at once, making training much faster
2. **Long-range dependencies**: Can effectively connect related information across very long sequences
3. **Transfer learning**: Pre-trained transformers can be fine-tuned for various tasks

## Real-World Applications

- **Language Models**: GPT, BERT, T5
- **Translation**: Google Translate
- **Computer Vision**: Vision Transformers (ViT)
- **Protein Folding**: AlphaFold

The transformer architecture is the foundation of most modern large language models and has become the dominant approach for processing sequential data across many domains.