The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations-making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.
Exploring Neural Networks with C# presents the important properties of neural networks-while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand.
Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks.
Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en
Rezensionen / Stimmen
This book offers a real-life experimentation environment to readers. Moreover, it permits direct and personal exploration of neural learning and modeling. The companion software to this book is a collection of online programs that facilitate such exploratory methods and systematic self-discovery of neural networks. The programs are available in two forms-as executable applications ready for immediate use as described in the book or as source codes in C#. ... As past president of IEEE's Computational Intelligence Society with over 6,000 members and the editor-in-chief of IEEE Transactions on Neural Networks, I am very interested in the success of neural network technology. I, therefore, highly recommend this book to all who want to learn neurocomputing techniques for their unique and practical educational value.-Dr. Jacek M. Zurada, Department of Electrical and Computer Engineering, University of Louisville, Kentucky This book offers a real-life experimentation environment to readers. Moreover, it permits direct and personal exploration of neural learning and modeling. The companion software to this book is a collection of online programs that facilitate such exploratory methods and systematic self-discovery of neural networks. The programs are available in two forms-as executable applications ready for immediate use as described in the book or as source codes in C#. ... As past president of IEEE's Computational Intelligence Society with over 6,000 members and the editor-in-chief of IEEE Transactions on Neural Networks, I am very interested in the success of neural network technology. I, therefore, highly recommend this book to all who want to learn neurocomputing techniques for their unique and practical educational value.-Dr. Jacek M. Zurada, Department of Electrical and Computer Engineering, University of Louisville, Kentucky
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Professional Practice & Development
Produkt-Hinweis
Broschur/Paperback
Klebebindung
Illustrationen
342 s/w Abbildungen
342 Illustrations, black and white
Maße
Höhe: 253 mm
Breite: 179 mm
Dicke: 25 mm
Gewicht
ISBN-13
978-1-4822-3339-1 (9781482233391)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Ryszard Tadeusiewicz, Rituparna Chaki, Nabendu Chaki
Introduction to Natural and Artificial Neural Networks. A Neural Net Structure. Teaching the networks. Functioning of a Simplest Network. Teaching Simple Linear One Layer Neural Networks. Nonlinear Networks. Backpropagation. Forms of Neural Networks Learning. Self-Learning Neural Networks. Self-Organizing Neural Networks. Recurrent Networks.
