Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)

by Daniel Graupe

Overview

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

Product Details

ISBN-13:	9789811201226
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	04/15/2019
Series:	Advanced Series In Circuits And Systems , #8
Pages:	440
Product dimensions:	6.69(w) x 9.61(h) x 1.00(d)

Table of Contents

Introduction and Role of Artificial Neural Networks; Fundamental Biological Neural Networks; Basic Principles; The Perceptron; The Madaline; Backpropagation; Hopfield Networks; Counter Propagation; Adaptive Resonance Theory; Cognitron and Neocognitron; Statistical Training; Recurrent Networks; Deep Learning Neural Networks: Principles and Scope; Convolutional Neural Networks; Large Memory Storage and Retrieval (LAMSTAR) Neural Networks; Performance of Deep Learning Neural Networks — Comparative Case Studies; Concluding Comments