Price: $36.50
(as of Sep 15, 2024 11:21:38 UTC – Details)
This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.FeaturesExtensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks.Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks.A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies.Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.
Publisher : Martin Hagan; 2nd ed. edition (September 1, 2014)
Language : English
Paperback : 800 pages
ISBN-10 : 0971732116
ISBN-13 : 978-0971732117
Item Weight : 2.98 pounds
Dimensions : 7.5 x 1.81 x 9.25 inches
Customers say
Customers find the book’s language good, clear, and concise. They say the content is self-contained and includes enough details for novices without much math. Readers also mention the chapters are well-designed and written. They appreciate the numerous illustrations, graphs, and summaries.
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