The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.

This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.

The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

Contents: Introduction and Role of Artificial Neural NetworksFundamentals of Biological Neural NetworksBasic Principles of ANNs and Their StructuresThe PerceptronThe MadalineBack PropagationHopfield NetworksCounter PropagationAdaptive Resonance TheoryThe Cognitron and NeocognitronStatistical TrainingRecurrent (Time Cycling) Back Propagation NetworksDeep Learning Neural Networks: Principles and ScopeDeep Learning Convolutional Neural NetworksLAMSTAR Neural NetworksPerformance of DLNN — Comparative Case Studies
Readership: Researchers, academics, professionals and senior undergraduate and graduate students in artificial intelligence, machine learning, neural networks and computer engineering.Neural Networks;Deep Learning;Artificial Intelligence;Machine Learning;Computer Engineering;Neurosciences;Medical Engineering;Image Processing;Signal Processing00

Daniel Graupe

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