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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book gives a systemic account of major concepts, methodologies of artificial neural networks and to present a unified frame work that makes the subject more accessible to students and practitioners. The book emphasizes fundamental theoretical aspects of the computational capabilities and learning abilities of artificial neural networks. It integrates important theoretical results on artificial neural networks and uses them to explain a wide range of existing empirical observations and commonly used heuristics. The main audience of the book is undergraduate students in electrical engineering, computer science and engineering. It can also be used as a valuable resource for practical engineering, computer scientists and others involved in research of artificial neural networks
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
This book gives a systemic account of major concepts, methodologies of artificial neural networks and to present a unified frame work that makes the subject more accessible to students and practitioners. The book emphasizes fundamental theoretical aspects of the computational capabilities and learning abilities of artificial neural networks. It integrates important theoretical results on artificial neural networks and uses them to explain a wide range of existing empirical observations and commonly used heuristics. The main audience of the book is undergraduate students in electrical engineering, computer science and engineering. It can also be used as a valuable resource for practical engineering, computer scientists and others involved in research of artificial neural networks