Architecture
Transformer
Architecture using attention mechanisms for sequence processing
What is Transformer?
Transformers are a type of neural network architecture introduced in 2017 that relies entirely on attention mechanisms to process sequential data. Unlike RNNs, transformers can process all positions in a sequence simultaneously, making them highly parallelizable and efficient.
Key Points
1
Uses self-attention mechanisms
2
Processes sequences in parallel
3
Foundation of modern LLMs
4
Revolutionized NLP and beyond
Practical Examples
GPT models
BERT
Vision Transformers
Text-to-image models