Introducción y optimización estocástica de redes neuronales profundas MLP

Abstract

[en] Since technology has stressed so much our society, human beings have always dreamed about to achieve the most. Among all those dreams, some more reachables than others, there is the ability to create machines that think by themselves. This chimera, despite of being quite different from how our ancestors ever imagined, is nowadays at the summit of its history. The massive increase of data, through digitalization, and the constant technological improvements, in this case, in the form of progress in high performance hardware production, have been the main drivers of this change. This grade project covers a specific area that is, sometimes, a part of the aforementioned creation process, known as Artificial Intelligence. This specific area is called Deep Learning. Deep learning techniques can be regarded as the algorithms that exploit data using models based on non-linear function compositions. In this respect, optimization plays a crucial role as it is the driver that transform the information in data into the model parameters. The project aims at understanding the mathematical basis of optimization, namely stochastic optimization theory and its application to deep learning.