This interdisciplinary graduate text gives a full, explicit, coherent and up-to-date account of the modern theory of neural information processing systems and is aimed at student with an undergraduate degree in any quantitative discipline (e.g. computer science, physics, engineering, biology, or mathematics). The book covers all the major theoretical developments from the 1940s tot he present day, using a uniform and rigorous style of presentation and of mathematical notation. The text starts with simple model neurons and moves gradually to the latest advances in neural processing. An ideal textbook for postgraduate courses in artificial neural networks, the material has been class-tested. It is fully self contained and includes introductions to the various discipline-specific mathematical tools as well as multiple exercises on each topic.
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The book provides an excellent class-tested material for graduate courses in artificial neural networks. It is completely self-contained and includes also thorough introduction to the discussed discipline-specific areas of mathematics...Therefore, this book represents a good reference source of applicable ideas for a wide audience including students, researchers and application specialists as well. EMS Newsletter graduate courses in artificial neural networks. It is completely self-contained and includes also a thorough introduction to the discussed discipline-specific areas of mathematics. Therefore, this book represents a good reference source of applicable ideas for a wide audience including students, researchers and application specialists as well. EMS Newsletter
About the Author
A.C.C. Coolen is at Professor of Applied Mathematics, Department of Mathematics, King's College, London. R. Kuehn is at Lecturer in Applied Mathematics, Department of Mathematics, King's College, London.
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