Overview
- Provides a concise and structured presentation of deep learning applications
- Introduces a large range of applications related to vision, speech, and natural language processing
- Includes active research trends, challenges, and future directions of deep learning
Part of the book series: Smart Innovation, Systems and Technologies (SIST, volume 136)
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About this book
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.
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Keywords
Table of contents (17 chapters)
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Front Matter
Editors and Affiliations
Bibliographic Information
Book Title: Handbook of Deep Learning Applications
Editors: Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui
Series Title: Smart Innovation, Systems and Technologies
DOI: https://doi.org/10.1007/978-3-030-11479-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-11478-7Published: 06 March 2019
eBook ISBN: 978-3-030-11479-4Published: 25 February 2019
Series ISSN: 2190-3018
Series E-ISSN: 2190-3026
Edition Number: 1
Number of Pages: VI, 383
Number of Illustrations: 54 b/w illustrations, 127 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Signal, Image and Speech Processing, Mathematical Models of Cognitive Processes and Neural Networks, Data Mining and Knowledge Discovery