# An Introduction to Transformers

**Authors:** Richard E. Turner  
**arXiv:** [2304.10557](https://arxiv.org/abs/2304.10557)  
**Date:** 2023-04-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2304.10557v1/

## Key points

- The Transformer is a neural network component that has made advancements in natural language processing, computer vision, and spatio-temporal modeling.
- Most introductions to transformers lack precise mathematical descriptions of the architecture and design choices.
- Richard E. Turner provides a mathematically precise and intuitive description of the transformer architecture in this note.
- The input data format for a transformer consists of sequences of tokens represented as a matrix.
- Tokens can be words or image patches embedded into vectors, with embeddings being fixed or learned along with other model parameters.
- The goal of the transformer is to learn useful representations through self-attention mechanisms and feed-forward neural networks within each layer.
- Self-attention allows weighing different parts of the input sequence when generating representations, while feed-forward neural networks introduce non-linear transformations.
- This architecture has driven recent advances in natural language processing, computer vision, and spatio-temporal modeling.
- The note aims to enhance understanding and facilitate further research on transformers by providing a mathematically precise and intuitive description.

## Summary

The Transformer is a neural network component that has made significant advancements in natural language processing, computer vision, and spatio-temporal modeling. However, most introductions to transformers lack precise mathematical descriptions of the architecture and often do not provide insights into the design choices. To address this gap in understanding, Richard E. Turner provides a mathematically precise and intuitive description of the transformer architecture in this note. The input data format for a transformer consists of sequences of tokens represented as a matrix. These tokens can be words embedded into vectors or image patches embedded into vectors; the embeddings can either be fixed or learned along with other model parameters. The goal of the transformer is to learn useful representations of sequences or sets of data points through multiple stages such as self-attention mechanisms and feed-forward neural networks within each layer. Self-attention allows the model to weigh different parts of the input sequence when generating representations while feed-forward neural networks introduce non-linear transformations. This architecture has been instrumental in driving recent advances in various fields such as natural language processing, computer vision, and spatio-temporal modeling. This note aims to enhance understanding and facilitate further research on transformers by providing a mathematically precise and intuitive description.