Recurrent Neural Networks
by Xiaolin Hu, P. Balasubramaniam
Publisher: InTech 2008
Number of pages: 400
The concept of neural network originated from neuroscience, and one of its primitive aims is to help us understand the principle of the central nerve system and related behaviors through mathematical modeling. The first part of the book is a collection of three contributions dedicated to this aim.
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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 Ivan F Wilde - King's College London
These notes are based on lectures given in the Mathematics Department at King's College London. An attempt has been made to present a logical (mathematical) account of some of the basic ideas of the 'artificial intelligence' aspects of the subject.
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 Rolf Pfeifer, Dana Damian, Rudolf Fuchslin - University of Zurich
Systematic introduction to neural networks, biological foundations; important network classes and learning algorithms; supervised models (perceptrons, adalines, multi-layer perceptrons), support-vector machines, echo-state networks, etc.