Definition
Introduced in the ‘Attention Is All You Need’ paper, the Transformer architecture revolutionized natural language processing and beyond. It uses multi-head self-attention to weigh the significance of different parts of the input data simultaneously, enabling efficient parallelization during training. This structure allows models to capture long-range dependencies effectively, forming the backbone of modern large language models like BERT and GPT series.
Summary
A deep learning architecture based on self-attention mechanisms that processes sequential data in parallel rather than sequentially.
Key Concepts
- Self-Attention
- Multi-Head Attention
- Positional Encoding
- Encoder-Decoder Structure
Use Cases
- Machine translation
- Text generation
- Image recognition (ViT)
Code Example
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