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 Jeff Heaton - Heaton Research
The book is an introduction to Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques are also introduced.
by Xiaolin Hu, P. Balasubramaniam - InTech
The concept of neural network originated from neuroscience, and one of its aims is to help us understand the principle of the central nerve system through mathematical modeling. The first part of the book is dedicated to this aim.
by Ben Goertzel - Plenum Press
This text applies the concepts of complexity science to provide an explanation of all aspects of human creativity. The book describes the model that integrates ideas from computer science, mathematics, neurobiology, philosophy, and psychology.
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.