  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.
