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books
| book details |
Deep Learning: Fundamentals, Theory and Applications
Edited by Kaizhu Huang, Edited by Amir Hussain, Edited by Qiu-Feng Wang, Edited by Rui Zhang
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| on special |
normal price: R 6 536.95
Price: R 6 209.95
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| book description |
The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
| product details |
Normally shipped |
Publisher | Springer Nature Switzerland AG
Published date | 5 Mar 2019
Language |
Format | Hardback
Pages | 163
Dimensions | 235 x 155 x 0mm (L x W x H)
Weight | 0g
ISBN | 978-3-0300-6072-5
Readership Age |
BISAC | medical / research
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