Algoritmos de Deep Learning utilizando Tensor Flow para el tratamiento de datos de producción científica en la Universidad Técnica de Cotopaxi - Falconí Punguil, Diego Geovanny (2021)
The Artificial Intelligence, Neural Networks and Deep Learning Algorithms implementation supported by Tensor Flow is currently in continuous evolution since have opened new routes for the treatment and analysis of data large amounts in systems mainly hosted on the web. Deep learning algorithms are responsible for training and grouping an unsupervised input data by similarity called machine learning, the same ones that model high-level abstractions using mainly data expressed in matrix form or tensors. This research purpose is to help the level of unsupervised decision making at Ecuciencia scientific platform, which is hosted on the Technical University of Cotopaxi servers. The data that will be taken as a reference for the analyzes introduced in the algorithms will be referring to Research Lines and Sublines according to the Technical University of Cotopaxi. The impact of Deep Learning Algorithms implementation supported by Tensor Flow in the Ecuciencia system will be very important, since, thanks to this analysis, the scientific platform will be able to give a more accurate prediction of the classifications of Research Lines and Sublines.