A Tutorial on Deep Neural Networks for Intelligent Systems
Abstract
Developing Intelligent Systems involves artificial intelligence approaches including artificial neural networks. Here, we present a tutorial of Deep Neural Networks (DNNs), and some insights about the origin of the term "deep"; references to deep learning are also given. Restricted Boltzmann Machines, which are the core of DNNs, are discussed in detail. An example of a simple two-layer network, performing unsupervised learning for unlabeled data, is shown. Deep Belief Networks (DBNs), which are used to build networks with more than two layers, are also described. Moreover, examples for supervised learning with DNNs performing simple prediction and classification tasks, are presented and explained. This tutorial includes two intelligent pattern recognition applications: hand- written digits (benchmark known as MNIST) and speech recognition.
Cite
@article{arxiv.1603.07249,
title = {A Tutorial on Deep Neural Networks for Intelligent Systems},
author = {Juan C. Cuevas-Tello and Manuel Valenzuela-Rendon and Juan A. Nolazco-Flores},
journal= {arXiv preprint arXiv:1603.07249},
year = {2016}
}
Comments
30 pages, 19 figures, unpublished technical report