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Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Download or Read eBook Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics PDF written by R. Sujatha and published by CRC Press. This book was released on 2021-09-22 with total page 217 pages. Available in PDF, EPUB and Kindle.
Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics
Author :
Publisher : CRC Press
Total Pages : 217
Release :
ISBN-10 : 9781000454536
ISBN-13 : 1000454533
Rating : 4/5 (36 Downloads)

Book Synopsis Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics by : R. Sujatha

Book excerpt: Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.


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