Neural Networks: A Systematic Introduction
by Raul Rojas
Publisher: Springer 1996
Number of pages: 509
Theoretical laws and models scattered in the literature are brought together in this book into a general theory of artificial neural nets. Starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. The book for readers who seek an overview of the field and wish to deepen their knowledge. Suitable as a basis for university courses in neurocomputing.
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by David Kriesel - dkriesel.com
Text and illustrations should be memorable and easy to understand to offer as many people as possible access to the field of neural networks. The chapters are individually accessible to readers with little previous knowledge.
by Todd Troyer - University of Texas at San Antonio
These notes have three main objectives: to present the major concepts of computational neuroscience, to present the basic mathematics that underlies these concepts, and to give the reader some idea of common approaches taken by neuroscientists.
by Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
by Milan Hajek - University of KwaZulu-Natal
Contents: Introduction; Learning process; Perceptron; Back-propagation networks; The Hopfield network; Self-organizing feature maps; Temporal processing with neural networks; Radial-basis function networks; Adaline (Adaptive Linear System).