9783540605058 - Neural Networks: A Systematic Introduction von Rojas, Raúl (7 Ergebnisse)

Softcover. 502 p. Good condition. The pages are clean and without markings. Slight signs of storage and use. Retired library copy with corresponding markings. Otherwise, a neat copy. 9783540605058 Sprache: Englisch Gewicht in Gramm: 1000.

Trade paperback. Trade paperback (US). 502 p. Contains: Illustrations. Audience: General/trade. Very good. No dust jacket as issued.

Zustand: New. In English.

Zustand: New. pp. 524 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Paperback. Zustand: Brand New. 1st edition. 522 pages. German language. 9.50x6.25x1.25 inches. In Stock.

Taschenbuch. Zustand: Neu. Neural Networks | A Systematic Introduction | Raul Rojas | Taschenbuch | xx | Englisch | 1996 | Springer | EAN 9783540605058 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing. Artificial neural networks are an alternative computational paradigm with roots in neurobiology which has attracted increasing interest in recent years. This book is a comprehensive introduction to the topic that stresses the systematic development of the underlying theory. Starting from simple threshold elements, more advanced topics are introduced, such as multilayer networks, efficient learning methods, recurrent networks, and self-organization. The various branches of neural network theory are interrelated closely and quite often unexpectedly, so the chapters treat the underlying connection between neural models and offer a unified view of the current state of research in the field.The book has been written for anyone interested in understanding artificial neural networks or in learning more about them. The only mathematical tools needed are those learned during the first two years at university. The text contains more than 300 figures to stimulate the intuition of the reader and to illustrate the kinds of computation performed by neural networks. Material from the book has been used successfully for courses in Germany, Austria and the United States.