Neural network design (265 resultados)

Condición: Good. [ No Hassle 30 Day Returns ][ Ships Daily ] [ Underlining/Highlighting: NONE ] [ Writing: NONE ] [ Edition: first ] Publisher: PWS Pub. Co. Pub Date: 12/29/1995 Binding: Hardcover Pages: 736 first edition.

Hardcover. Condición: Bon. Ancien livre de bibliothèque avec équipements. Edition 1996. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Edition 1996. Ammareal gives back up to 15% of this item's net price to charity organizations.…

Condición: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condición: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Condición: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Hardcover. Condición: Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Condición: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Paperback. Condición: Good. 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. Features Extensive 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.…

Paperback. Condición: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Paperback. Condición: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority.

Paperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condición: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.

Condición: very_good. Fast Free Shipping â" Very Good condition book with a firm cover and clean pages. Shows normal use and some light wear or limited notes markings. A solid, nice copy to enjoy.

Paperback. Condición: Good. 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. Features Extensive 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.…

Hardcover. Condición: Good. No Jacket. Please note the image in this listing is a stock photo and may not match the covers of the actual item. Book.

Condición: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1500grams, ISBN:9780534943325.…

Condición: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1550grams, ISBN:9780534943325.…

Hardcover. Condición: Used; Very Good. **SHIPPED FROM UK** We believe you will be completely satisfied with our quick and reliable service. All orders are dispatched as swiftly as possible! Buy with confidence! Greener Books.

Condición: very_good. Gently read. May have name of previous ownership, or ex-library edition. Binding tight; spine straight and smooth, with no creasing; covers clean and crisp. Minimal signs of handling or shelving. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships USPS Media Mail.…

Paperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Condición: New.

paperback. Condición: New.

Paperback or Softback. Condición: New. Neural Network Design (2nd Edition). Book.

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: New. 2nd ed.

Condición: New.

Condición: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.

Condición: very good. Cambridge, MA : The MIT Press, 1990. Hardcover. Dustjacket. 170 pp.Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Neural Network Modeling and ConnectionismEnglish text. Condition : very good. Neural Network Modeling and Connectionism Condition : very good copy. ISBN 9780262100458. Keywords : .…

Paperback or Softback. Condición: New. Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and Tensorflow Models Using Python. Book.