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In response to an increasing demand for novel computing methods, Neural Networks for Applied Sciences and Engineering provides a simple but systematic introduction to neural networks applications. This book features case studies that use real data to demonstrate practical applications. It contains in-depth discussions of data and model validation issues along with uncertainty and sensitivity assessment of models as well as data dimensionality and methods to reduce dimensionality. It provides detailed coverage of neural network types for extracting nonlinear patterns in multi-dimensional scientific data in prediction, classification, clustering and forecasting with an extensive coverage on linear networks, multi-layer perceptron, self organization maps, and recurrent networks.
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Previews available in: English
Subjects
Computer Technology, Nonfiction, Neural networks (computer science), Neural networks (Computer science), Pattern recognition systems, Computer Neural Networks, Automated Pattern Recognition, Réseaux neuronaux (Informatique), Reconnaissance des formes (Informatique), COMPUTERS, Neural NetworksShowing 3 featured editions. View all 3 editions?
Edition | Availability |
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1
Neural Networks for Applied Sciences and Engineering: from Fundamentals to Complex Pattern Recognition
2010, Taylor & Francis Group
in English
128106548X 9781281065483
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2
Neural Networks for Applied Sciences and Engineering
2006, Taylor and Francis
Electronic resource
in English
1420013068 9781420013061
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WorldCat
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3
Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition
September 12, 2006, AUERBACH
Hardcover
in English
- 1 edition
084933375X 9780849333750
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