The Transformer architecture or sometimes called just Transformers it’s a revolutionary way to build neural network based on attention mechanisms, it allows to process an entire sequence of text in parallel and allowing to capture relationships between elements independently of their distance in the sequence.
It was introduced by first time on a famous paper called “Attention is all you need” published in 2017 by Vaswani et al at Google. Before Transformers, the traditional approach where to use Recurrent or Convolutional neural networks.
Transformers are the foundation of modern Large Language Models we use today, like GPT, BERT, Claude, and many others.
Key Aspects
- ✅ Transformers use attention mechanisms
- ✅ Process text in parallel (not sequential)
- ✅ Foundation of modern LLMs
- ✅ Enable transfer learning
- ✅ Scale well (bigger = better)
References
Attention Is All You Need, famous paper of 2017
Related Notes
- Transformer Architecture Components
- Attention Mechanism
- Transformers vs RNNs
- Transformer Architecture Advantages