Daniel Graupe

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance.

This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.

Contents:

• Acknowledgements
• Preface
• Deep Learning Neural Networks: Methodology and Scope
• Basic Concepts of Neural Networks
• Back Propagation
• The Cognitron and Neocognitron
• Deep Learning Convolutional Neural Networks
• LAMSTAR-1 and LAMSTAR-2 Neural Networks
• Other Neural Networks for Deep Learning
• Case Studies
• Concluding Comments
• Problems
• Appendices to Case Studies of Chapter 8
• Author Index
• Subject Index

Readership: Researchers, academics, professionals, graduate and undergraduate students in machine learning, artificial intelligence, neural networks/networking, software engineering, and in their applications in medicine, security engineering and financial engineering.