“The Transformer architecture is a deep learning model that processes entire data sequences in parallel, using an attention mechanism to weigh the significance of different elements in the sequence.” – Transformer architecture
Definition
The **Transformer architecture** is a deep learning model that processes entire data sequences in parallel, using an attention mechanism to weigh the significance of different elements in the sequence.1,2
It represents a neural network architecture based on multi-head self-attention, where text is converted into numerical tokens via tokenisers and embeddings, allowing parallel computation without recurrent or convolutional layers.1,3 Key components include:
- Tokenisers and Embeddings: Convert input text into integer tokens and vector representations, incorporating positional encodings to preserve sequence order.1,4
- Encoder-Decoder Structure: Stacked layers of encoders (self-attention and feed-forward networks) generate contextual representations; decoders add cross-attention to incorporate encoder outputs.1,5
- Multi-Head Attention: Computes attention in parallel across multiple heads, capturing diverse relationships like syntactic and semantic dependencies.1,2
- Feed-Forward Layers and Residual Connections: Refine token representations with position-wise networks, stabilised by layer normalisation.4,5
The attention mechanism is defined mathematically as:
Attention(Q, K, V) = softmax\left( \frac{QK^T}{\sqrt{d_k}} \right) Vwhere Q, K, V are query, key, and value matrices, and d_k is the dimension of the keys.1
Introduced in 2017, Transformers excel in tasks like machine translation, text generation, and beyond, powering models such as BERT and GPT by handling long-range dependencies efficiently.3,6
Key Theorist: Ashish Vaswani
Ashish Vaswani is a lead author of the seminal paper “Attention Is All You Need”, which introduced the Transformer architecture, fundamentally shifting deep learning paradigms.1,2
Born in India, Vaswani earned his Bachelor’s in Computer Science from the Indian Institute of Technology Bombay. He pursued a PhD at the University of Massachusetts Amherst, focusing on machine learning and natural language processing. Post-PhD, he joined Google Brain in 2015, where he collaborated with Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, ?ukasz Kaiser, and Illia Polosukhin on the Transformer paper presented at NeurIPS 2017.1
Vaswani’s relationship to the term stems from co-inventing the architecture to address limitations of recurrent neural networks (RNNs) in sequence transduction tasks like translation. The team hypothesised that pure attention mechanisms could enable parallelisation, outperforming RNNs in speed and scalability. This innovation eliminated sequential processing bottlenecks, enabling training on massive datasets and spawning the modern era of large language models.2,6
Currently a research scientist at Google, Vaswani continues advancing AI efficiency and scaling laws, with his work cited over 100,000 times, cementing his influence on artificial intelligence.1
References
1. https://en.wikipedia.org/wiki/Transformer_(deep_learning)
2. https://poloclub.github.io/transformer-explainer/
3. https://www.datacamp.com/tutorial/how-transformers-work
4. https://www.jeremyjordan.me/transformer-architecture/
5. https://d2l.ai/chapter_attention-mechanisms-and-transformers/transformer.html
6. https://blogs.nvidia.com/blog/what-is-a-transformer-model/
7. https://www.ibm.com/think/topics/transformer-model
8. https://www.geeksforgeeks.org/machine-learning/getting-started-with-transformers/
