Deep Learning: An Overview
Armando S. Vieira, Bernardete Ribeiro · Apress eBooks · 2018
Artificial neural networks are not new; they have been around for about 50 years and got some practical recognition after the mid-1980s with the introduction of a method (backpropagation) that allowed for the training of multiple-layer neural networks. However, the true birth of deep learning may be traced to the year 2006, when Geoffrey Hinton [GR06] presented an algorithm to efficiently train deep neural networks in an unsupervised way—in other words, data without labels. They were called deep belief networks (DBNs) and consisted of staked restrictive Boltzmann machines (RBMs), with each one placed on the top of another. DBNs differ from previous networks since they are generative models capable of learning the statistical properties of data being presented without any supervision. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.