The applications in this field serve as a major decision tool in Big Data applications. DLNN 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. Their range of applications covers almost any problem whose input data, performance evaluation and target decision can be numerically expressed.
The architecture of DLNN is based on principles of biological neural networks and have the ability to intelligently integrate any mathematical or logical algorithm into their decision process.
The book's contents includes basic concepts of neural networks, back propagation, cognitron and neocognitron, deep learning convolutional neural networks, LAMSTAR-1 and LAMSTAR-2 neural networks, and case studies, amongst others.
Authored by Daniel Graupe from the University of Illinois, Chicago, USA, "Deep Learning Neural Networks: Design and Case Studies" is on sale in major bookstores, including Amazon.
This book retails at US$88 / GBP73 (hardcover) and US$48 / GBP40 (paperback). More information can be found at http://www.worldscientific.com/worldscibooks/10.1142/10190 .