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Titlebook: Deep Learning: Convergence to Big Data Analytics; Murad Khan,Bilal Jan,Haleem Farman Book 2019 The Author(s), under exclusive license to S

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發(fā)表于 2025-3-21 16:26:24 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Deep Learning: Convergence to Big Data Analytics
編輯Murad Khan,Bilal Jan,Haleem Farman
視頻videohttp://file.papertrans.cn/265/264644/264644.mp4
概述Offers an introduction to big data and deep learning.Presents a unification of big data and deep learning techniques.Provides an introductory level understanding of the new programming languages and t
叢書(shū)名稱(chēng)SpringerBriefs in Computer Science
圖書(shū)封面Titlebook: Deep Learning: Convergence to Big Data Analytics; Murad Khan,Bilal Jan,Haleem Farman Book 2019 The Author(s), under exclusive license to S
描述.This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning..Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses var
出版日期Book 2019
關(guān)鍵詞Deep Learning; Big Data analytics; Neural Networks; Artificial Intelligence; Internet of Things; data str
版次1
doihttps://doi.org/10.1007/978-981-13-3459-7
isbn_softcover978-981-13-3458-0
isbn_ebook978-981-13-3459-7Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019
The information of publication is updating

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Deep Learning Methods and Applications,n various fields. Deep learning has substantially improved the predictive capacity of computing devices, due to the availability of big data, with the help of superior learning algorithms. It has made it possible as well as practical to integrate machine learning with sophisticated applications incl
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Integration of Big Data and Deep Learning,rchers introduce the concept of deep learning to address the aforementioned challenge. However, big data analytics required a process consists of various steps where in each step an algorithm or a bunch of algorithm can be used. This chapter explains the role of machine learning in processing big da
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Future of Big Data and Deep Learning for Wireless Body Area Networks,data. It has the ability to find the optimum set of parameters for the network layers using a back-propagation algorithm, thereby modeling intricate structures in the data distribution. Further, deep learning architectures have resulted in tremendous performance on most recent machine learning chall
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2191-5768 y level understanding of the new programming languages and t.This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time
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