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Deep Learning Neural Networks: Design and Case Studies, by Daniel Graupe
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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.
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.
- Sales Rank: #815979 in Books
- Published on: 2016-08-02
- Original language: English
- Dimensions: 9.70" h x .60" w x 6.50" l, .0 pounds
- Binding: Paperback
- 280 pages
Most helpful customer reviews
0 of 0 people found the following review helpful.
A Great Graduate-Level Textbook, Not a Programming Guide.
By Rachel Baker
Excellent graduate-level/researcher’s textbook. However, it is not for those looking for a programming guide. The text covers the major
deep-learning neural networks, their background and applications. Analysis and derivation of the 3 major such networks including 100+
mathematical equations. It has the first detailed exposition of the LAMSTAR network. A much-needed comparison of speed and accuracy of the major deep learning networks in 20 applications is included, Sample programs are appended.
0 of 0 people found the following review helpful.
Don't waste your money in this book
By Amazon Customer
Don't waste your money in this book. The book just lists out some technical details without providing any insights or intuition or practical guidance. Also I don't know what's the point of including all those codes in the book. They could've put them on a website. Moreover, the kindle version is full of typos. I don't know whether the printed version is as bad.
5 of 6 people found the following review helpful.
This book lacks of "deep" explanation of the subjects it ...
By TheReader
This book lacks of "deep" explanation of the subjects it is trying to cover. Mathematical expressions and neural networks topologies are barely explained. It resembles more a dictionary of terms with general annotations and comments rather than a book one can base to learn how to design and implement neural networks, specifically CNNs. If the price of this book were much less it may be worth it to buy it.
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