A Tutorial on Deep Neural Networks for Intelligent Systems
It serves as an educational resource for those interested in AI and intelligent systems, but it is incremental as it primarily reviews existing methods without introducing new research.
This tutorial provides an overview of Deep Neural Networks (DNNs), covering their components like Restricted Boltzmann Machines and Deep Belief Networks, and demonstrates their application in pattern recognition tasks such as MNIST digit classification and speech recognition.
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.