Transformadores: Fundamentos teoricos y Aplicaciones

Transformers are a neural network architecture originally designed for natural language processing that it is now a mainstream tool for solving a wide variety of problems, including natural language processing, sound, image, reinforcement learning, and other problems with heterogeneous input data. Its distinctive feature is its self-attention system, based on attention to one's own sequence, which derives from the previously introduced attention system. This article provides the reader with the necessary context to understand the most recent research articles and presents the mathematical and algorithmic foundations of the elements that make up this type of network. The different components that make up this architecture and the variations that may exist are also studied, as well as some applications of the transformer models. This article is in Spanish to bring this scientific knowledge to the Spanish-speaking community.