A termék adatai:
ISBN13: | 9781857286731 |
ISBN10: | 1857286731 |
Kötéstípus: | Keménykötés |
Terjedelem: | 248 oldal |
Méret: | 234x156 mm |
Súly: | 498 g |
Nyelv: | angol |
0 |
Témakör:
Kombinatorika és gráfelmélet
Villamosmérnöki tudományok, híradástechnika, műszeripar
Energetika, energiaipar
Rendszerszervezés
Környezetmérnöki tudományok
Diszkrét matematika
Kombinatorika és gráfelmélet (karitatív célú kampány)
Villamosmérnöki tudományok, híradástechnika, műszeripar (karitatív célú kampány)
Energetika, energiaipar (karitatív célú kampány)
Rendszerszervezés (karitatív célú kampány)
Környezetmérnöki tudományok (karitatív célú kampány)
Diszkrét matematika (karitatív célú kampány)
An Introduction to Neural Networks
Kiadás sorszáma: 1
Kiadó: CRC Press
Megjelenés dátuma: 1997. augusztus 5.
Normál ár:
Kiadói listaár:
GBP 75.00
GBP 75.00
Az Ön ára:
35 438 (33 750 Ft + 5% áfa )
Kedvezmény(ek): 10% (kb. 3 938 Ft)
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Rövid leírás:
Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. The author tackles virtually all aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods; associative memory and Hopfield nets; and self-organization and feature maps. The book provides a concrete focus through several real-world examples. This feature broadens the book's audience to include both students and professionals in cognitive science, psychology, and computer science as well as those involved in the design, construction, and management of networks.
Hosszú leírás:
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
Tartalomjegyzék:
Neural Net - A Preliminary Discussion. The von Neumann Machine and The Symbolic Paradigm. Real Neurons - A Review. Artificial neurons. Non- binary signal communication. Introducing Time. Network Features. Alternative Node Types. Cubic Nodes and Reward. Penalty Training. Drawing Things Together - Some Perspectives.