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Concepts and Real-Time Applications of Deep Learning, 1st ed. 2021 EAI/Springer Innovations in Communication and Computing Series

Langue : Anglais
Couverture de l’ouvrage Concepts and Real-Time Applications of Deep Learning

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning architecture with applications in natural language processing, semantic knowledge, forecasting and many more.  The authors shed light on various applications that can benefit from the use of deep learning in pattern recognition, person re-identification in surveillance videos, action recognition in videos, image and video captioning. The book also highlights how deep learning concepts can be interwoven with more modern concepts to yield applications in multidisciplinary fields.

  • Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures;
  • Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies;
  • Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches.

Introduction.- AI Techniques based on Modern Deep Learning architectures.- Application of Deep learning in Optimization and Regression.- Deep learning in Pattern recognition.- Application of Deep Learning in Healthcare systems for diagnosing disease.- Application of Deep Learning for Security and threats.- Deep Learning Framework and Tools.- Advancements in Deep Learning.- Multidisciplinary Applications of Machine/Deep Learning.- Conclusion.

Prof. Smriti Srivastava received the B.E. degree in electrical engineering and the M.Tech. degree in heavy electrical equipment from Maulana Azad College of Technology [now Maulana Azad National Institute of Technology (MANIT)], Bhopal, India, in 1987 and 1991, respectively, and the Ph.D. degree in intelligent control from the Indian Institute of Technology, New Delhi, India, in 2005. From 1988 to 1991, she was a faculty member with MANIT, and since August 1991, she has been with the Department of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, University of Delhi, New Delhi, India, where she is presently working as Professor in the Instrumentation and Control Engineering Division at NSIT, New Delhi from September 2008 to till date and Dean Under Graduate Studies. She also worked as Associate Head of Instrumentation & Control Engineering Division at NSIT, New Delhi from April 2004 to Nov. 2007 and from September 2008 to December 2011. She is the author of a number of publications in Transactions, journals and conferences in the areas of neural networks, fuzzy logic, and control systems. She has given a number of invited talks and tutorials mostly in the area of fuzzy logic, process control, and neural networks. Her current research interests include neural networks, fuzzy logic, and hybrid methods in modeling, identification and control of nonlinear systems. She is the reviewer of Reviewer of IEEE Transactions on Systems, Man and Cybernetics (SMC), Part-B. IEEE Transactions on Fuzzy Systems, International Journal of Applied Soft Computing (Elsevier), International Journal of Energy, Technology and Policy (Inder Science). She is the member of World Scientific and Engineering Academy and Society (WSEAS) working committee on computers. She is also on the Editorial board of Scientific and Academic publishing

 

Dr. Manju Khari Is an Assistant Professor in Ambedkar Institute of Advanced Communication Technology and Resear
Presents a comprehensive look at deep learning and its multidisciplinary applications, concentrating on advances of deep learning architectures Includes a survey of deep learning problems and solutions, identifying the main open issues, innovations and latest technologies Shows industrial deep learning in practice with examples/cases, efforts, challenges, and strategic approaches

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Ouvrage de 209 p.

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