Robinson Jimenez  Moreno, Ruben D. Hernandez  Beleno, Natalie Segura  Velandia, 
    Applications of Deep Neural Networks,
    International Journal of System Signal Control and Engineering Application,
    Volume 10,Issue 1,
    2017,
    Pages 61-76,
    ISSN 1997-5422,
    ijssceapp.2017.61.76,
    (https://makhillpublications.co/view-article.php?doi=ijssceapp.2017.61.76)
    Abstract: This study presents a review of the computational methods implemented with the deep learning
technique, highlighting the architecture of convolutional neural networks. Works that use these methods are
addressed for image recognition and pattern recognition, each characterizing a specific task. With the
technological and investigative momentum that has been generated today and with the development of systems
with the capacity to process data with advanced intelligence equivalent to that of the human being, it has
awakened an awareness of technological applications in all sectors from business to health where data
processing takes place at times less than the human brain could do. In this way, deep learning is an
approximation to human perception based on the hierarchical functioning of the neocortex, a fundamental part
of the human brain. This technique relies on computational models composed of several layers of processing
to learn the representations of data with several levels of abstraction, possessing the ability to modify its
internal parameters which are used to calculate the representation in each layer from the previous layer.
    Keywords: recurrent neural networks;convolutional neural network;Boltzmann machine;autoencoder;Deep learning;deep belief network