Neural Networks: A Comprehensive Foundation: United States Edition

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Neural Networks: A Comprehensive Foundation: United States Edition

← Free Neural Networks: A Comprehensive Foundation: United States Edition ⇭ Kindle Ebook By Simon O Haykin − For graduate level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.Renowned for its thoroughness and readability, this well organized and completely up to date text remains the most comprehensive treatment of neural networks from an engineering perspective Thoroughly revised.Renowned for its thoroughness and readability, this well organized and completely up to date text remains the most comprehensive treatment of neural networks from an engineering perspective Thoroughly revised.NEW TO THIS EDITIONNEW New chapters now cover such areas as Support vector machines Reinforcement learning neurodynamic programming Dynamically driven recurrent networks NEW End of chapter problems revised, improved and expanded in number FEATURES Extensive, state of the art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology s capabilities and potential applications Detailed analysis of back propagation learning and multi layer perceptrons Explores the intricacies of the learning process an essential component for understanding neural networks Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary Includes a detailed and extensive bibliography for easy reference Computer oriented experiments distributed throughout the book Uses Matlab SE version 5. 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