Elements of Artificial Neural Networks provides a clearly organized general introduction, focusing on a broad range of algorithms, for students and others who want to use neural networks rather than simply study them.
The authors, who have been developing and team teaching the material in a one-semester course over the past six years, describe most of the basic neural network models (with several detailed solved examples) and discuss the rationale and advantages of the models, as well as their limitations. The approach is practical and open-minded and requires very little mathematical or technical background. Written from a computer science and statistics point of view, the text stresses links to contiguous fields and can easily serve as a first course for students in economics and management.
The opening chapter sets the stage, presenting the basic concepts in a clear and objective way and tackling important—yet rarely addressed—questions related to the use of neural networks in practical situations. Subsequent chapters on supervised learning (single layer and multilayer networks), unsupervised learning, and associative models are structured around classes of problems to which networks can be applied. Applications are discussed along with the algorithms. A separate chapter takes up optimization methods.
The most frequently used algorithms, such as backpropagation, are introduced early on, right after perceptrons, so that these can form the basis for initiating course projects. Algorithms published as late as 1995 are also included. All of the algorithms are presented using block-structured pseudo-code, and exercises are provided throughout. Software implementing many commonly used neural network algorithms is available at the book's website.
Transparency masters, including abbreviated text and figures for the entire book, are available for instructors using the text.
Downloadable instructor resources available for this title: solution manual and slides
HardcoverOut of Print ISBN: 9780262133289 360 pp. | 9 in x 7 in
Elements of Artificial Neural Networks is appropriate as a text for a senior level class for engineering and/or computer science students. It is also likely to be used by students in economics and management. The authors have done a very good job in describing many of the popular network structures, with several detailed solved examples. The lucid writing style makes the book accessible to a wide range of students and fills the need for a sound engineering oriented senior-level text in this exciting area.
Associate Professor and Endowed Engineering Foundation Fellow, The University of Texas at Austin
Elements of Artificial Neural Networks is very well written, provides enough detail to allow students to implement various algorithms, and contains good examples. A nice mix of elementary discussion with a good breadth of coverage, it will be appropriate as an undergraduate text.
Andrew G. Barto
Professor, Department of Computer Science University of Massachusetts
Elements of Artificial Neural Networks is a promising text. Well written and quite readable, it will be a good introduction for an undergraduate or someone who wants to use neural networks.
Project Manager, Adaptive Information and Signal Processing, Siemens Corporate Research, Inc.
This most readable book gives a clear, up-to-date and concise introduction to artificial neural networks. It covers all the major network models and provides insightful information on their applications. I thoroughly recommend it to senior undergraduates, first-year graduate students and practising engineers seeking an accessible lead-in to this fast expanding field.
Duc Truong Pham
Professor and Director of the Intelligent Systems Laboratory, School of Engineering, University of Wales Cardiff, United Kingdom