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| ISBN 13 | |
|---|---|
| ISBN 10 | |
| Edition | |
| No. of Pages | |
| Type of book | |
| Weight | 400.00 g |
| Year | |
| Language | |
| Publisher | |
| Save | ₹68.75 (25% Off) |
| Price |
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| Categories | Computer Science Engineering |
| Condition Type | New |
| Country Origin | India |
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Written with the beginner in mind, this volume offers an exceptionally clear and thorough introduction to neural network at an elementary level. It provides the clear survey of basic neural network architectures and learning rules. In it, We emphasize mathematical analysis of networks, methods for training network, and application of neural network to practical engineering problems in speech recognition, stock market prediction and pattern recognition.
Chapter 1: Fundamentals of Neural Networks.
Chapter 2: Perceptrons.
Chapter 3: Backpropagation.
Chapter 4: Adaline and Madaline.
Chapter 5: Supervised and Unsupervised Learning.
Chapter 6: Counter-propagation Network.
Chapter 7: Adaptive Resonance Theory.
Chapter 8: Neocognitron.
Chapter 9: Bidirectional Associative Memory.
Chapter 10: Case Studies.
Ikvinderpal Singh
Ikvinderpal Singh, is Lecturer of P.G. Deptt. Of Computer Science & Applications, Khalsa College, Amritsar which is a premier institute in North India. He obtained his MCA with distinction from Guru Nanak Dev University, Amritsar. He has always been excellence right from his student carrier. He has written five books. He brought name for himself when he topped the college in B.Sc. His other areas of interest include Fuzzy systems, digital electronics and java programming.
Rachhpal Singh
Rachhpal Singh is head of P.G. Dept. Computer Science & Applications, Khalsa College, Amritsar which is a premier institute in North India. He obtained his MCA from thapar Institute of Engineering and Technology, patiala. He has over fifteen years of teaching experience to teach post graduate classes. He is an international author of the world level and his other areas of interest include software engineering, parallel processing system level programming.
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Written with the beginner in mind, this volume offers an exceptionally clear and thorough introduction to neural network at an elementary level. It provides the clear survey of basic neural network architectures and learning rules. In it, We emphasize mathematical analysis of networks, methods for training network, and application of neural network to practical engineering prob...
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