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portada Deep Learning Techniques and Optimization Strategies in big Data Analytics (Advances in Systems Analysis, Software Engineering, and High Performance Computing)
Type
Physical Book
Illustrated by
Publisher
Author
Year
2019
Language
English
Pages
380
Format
Hardcover
Dimensions
27.9 x 21.6 x 2.2 cm
Weight
1.18 kg.
ISBN13
9781799811923
Categories

Deep Learning Techniques and Optimization Strategies in big Data Analytics (Advances in Systems Analysis, Software Engineering, and High Performance Computing)

J. Joshua Thomas (Illustrated by) · Pinar Karagoz (Illustrated by) · B. Bazeer Ahamed (Illustrated by) · Engineering Science Reference · Hardcover

Deep Learning Techniques and Optimization Strategies in big Data Analytics (Advances in Systems Analysis, Software Engineering, and High Performance Computing) - Thomas, J. Joshua ; Karagoz, Pinar ; Ahamed, B. Bazeer

New Book Imported to South Africa *
Delivery: 24 Apr - 19 May Shipping: 3 to 4 business days.
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R 5,615.43
Delivery to any South Africa address between Friday, April 24 and Tuesday, May 19
Origin: United Kingdom Import costs included in the price ✅
Delivery to any South Africa address between Friday, April 24 and Tuesday, May 19.

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Synopsis "Deep Learning Techniques and Optimization Strategies in big Data Analytics (Advances in Systems Analysis, Software Engineering, and High Performance Computing) "

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

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