Deep Learning Algorithms using Tensorflow for Processing Scientific Production Data
DOI:
https://doi.org/10.55204/trc.v3i2.e226Keywords:
Algorithms, Neural Networks, Deep Learning, TensorFlow, KDDAbstract
Introduction: The implementation of Artificial Intelligence, Neural Networks and Deep Learning Algorithms supported by TensorFlow is currently in constant evolution since they have opened new routes for the treatment and analysis of large amounts of data in systems mainly hosted on the web.
Objective: The purpose of this research is to help the level of unsupervised decision making in the scientific platform Ecuciencia, which is hosted on the servers of the Technical University of Cotopaxi.
Method: The data that will be taken as a reference for the analyzes introduced in the algorithms will be those referring to Research Lines and Sublines according to the Technical University of Cotopaxi.
Results: 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.
Conclusion: The impact of the implementation of Deep Learning Algorithms supported by TensorFlow 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.
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