Understanding Transformers and Attention Mechanisms
Transformers NLP Deep Learning
The Transformer architecture, introduced in the paper “Attention is All You Need”, revolutionized natural language processing and is now the foundation for models like BERT, GPT, and LLaMA.
The Key Insight: Self-Attention
The self-attention mechanism allows the model to weigh the importance of different parts of the input when producing each output element.
Why Transformers Matter
- Parallelization - Unlike RNNs, transformers can process all positions simultaneously
- Long-range dependencies - Attention can relate distant positions in a sequence
- Scalability - The architecture scales well with more data and compute
Understanding transformers is essential for anyone working in modern NLP and increasingly in computer vision as well.