Transformers are a neural network architecture originally developed for natural language processing, which have since become a foundational tool for solving a wide range of problems, including text, audio, image processing, reinforcement learning, and other tasks involving heterogeneous input data. Their hallmark is the self-attention mechanism, which allows the model to weigh different parts of the input sequence dynamically, and is an evolution of earlier attention-based approaches. This article provides readers with the necessary background to understand recent research on transformer models, and presents the mathematical and algorithmic foundations of their core components. It also explores the architecture's various elements, potential modifications, and some of the most relevant applications. The article is written in Spanish to help make this scientific knowledge more accessible to the Spanish-speaking community.
View on arXiv@article{torre2025_2302.09327,
title={ Transformadores: Fundamentos teoricos y Aplicaciones },
author={ Jordi de la Torre },
journal={arXiv preprint arXiv:2302.09327},
year={ 2025 }
}