An Introduction to Neural Networks
Seiten
1997
Routledge (Verlag)
978-1-85728-503-1 (ISBN)
Routledge (Verlag)
978-1-85728-503-1 (ISBN)
Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. It tackles various aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; and gradient descent methods.
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.
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.
Gurney; K. University of Sheffield, UK,
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.
| Erscheint lt. Verlag | 5.8.1997 |
|---|---|
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 380 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-85728-503-4 / 1857285034 |
| ISBN-13 | 978-1-85728-503-1 / 9781857285031 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
Eine praxisorientierte Einführung
Buch | Softcover (2025)
Springer Vieweg (Verlag)
CHF 53,15
Buch | Softcover (2025)
Reclam, Philipp (Verlag)
CHF 11,20
die materielle Wahrheit hinter den neuen Datenimperien
Buch | Hardcover (2024)
C.H.Beck (Verlag)
CHF 44,75
