Deep Learning: Convergence to Big Data Analytics
暫譯: 深度學習：收斂於大數據分析
Khan, Murad, Jan, Bilal, Farman, Haleem
- 出版商: Springer
- 出版日期: 2019-01-10
- 售價: $2,810
- 貴賓價: 9.5 折 $2,670
- 語言: 英文
- 頁數: 79
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9811334587
- ISBN-13: 9789811334580
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相關分類:
大數據 Big-data、DeepLearning、Data Science
海外代購書籍(需單獨結帳)
相關主題
商品描述
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 various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues.
The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
商品描述(中文翻譯)
這本書介紹了深度學習技術、概念和算法，以分類和分析大數據。此外，它提供了對用於實時分析大數據的新程式語言和工具的入門理解，例如 Hadoop、SPARK 和 GRAPHX。使用傳統技術進行大數據分析面臨各種挑戰，例如在實時中快速、準確和高效地處理大數據。此外，物聯網在智能城市、智能家居和電子健康等各個領域逐漸增長。隨著大量連接設備每天產生巨量數據，我們需要複雜的算法來處理、組織和分類這些數據，以減少處理時間和空間。同樣，現有的深度學習技術和算法在大數據領域具有多種優勢，這得益於深度學習的兩個主要分支，即卷積神經網絡和深度信念網絡。本書提供了基於這兩種類型深度學習的技術和應用的見解。
此外，它幫助學生、研究人員和新手理解基於深度學習方法的大數據分析。它還討論了各種機器學習技術與深度學習範式的結合，以支持高端數據處理、數據分類和實時數據處理問題。
分類和呈現保持相當簡單，以幫助讀者和學生掌握各種深度學習範式和框架的基本概念。它主要專注於理論，而不是深度學習概念的數學背景。這本書由五章組成，首先介紹大數據和深度學習技術的基本解釋，接著是大數據與深度學習技術的整合，最後是未來的方向。
作者簡介
Murad Khan received a B.S. degree in Computer Science from the University of Peshawar Pakistan in 2008. He completed his Ph.D. in Computer Science and Engineering at the School of Computer Science and Engineering at Kyungpook National University, Daegu, Korea. Dr. Khan has published over 50 international conference and journal papers along with two books chapters with Springer and CRC Press. He also served as a TPC member in reputable international conferences, such as ACM SAC 2017, ICFNDS 2017, and as a reviewer for numerous journals such as Future Generation Systems (Elsevier) and IEEE Access. In 2016, he received the Kyungpook National University's Qualcomm Innovation Award for designing a smart home control system. He was also awarded the Bronze Medal at ACM SAC 2015, Salamanca, Spain, for his work on multi-criteria based handover techniques. He is a member of various communities, including ACM and IEEE, and CRC Press. His areas of expertise include ad-hoc and wireless networks, architecture design for Internet of Things, and communication protocol design for smart cities and homes, big data analytics, etc.
Bilal Jan received his M.S. and Ph.D. degrees from the Department of Control and Computer Engineering (DAUIN) Politecnico di Torino, Italy, in 2010 and 2015 respectively. He has published several papers in reputed journals and conferences. He is currently working as Assistant Professor and Head of the Department of Computer Science, FATA University, Darra Adam Khel, FR Kohat, Pakistan. He is a reviewer for numerous leading journals. His research interests include general purpose programming in GPUs, high-performance computing, wireless sensor networks, Internet of things (IoT), deep learning and big data.
Haleem Farman received his M.S. degree from the International Islamic University, Islamabad, Pakistan in 2008. He is currently pursuing his Ph.D. degree in Computer Science at the Department of Computer Science, University of Peshawar, Pakistan, and working as a lecturer at the Department of Computer Science, Islamia College Peshawar, Pakistan. He has authored/co-authored more than 20 research papers in respected journals and conferences. In addition, he serves as an invited reviewer for several journals, such as Elsevier Sustainable Cities and Society. His fields of interest include wireless sensor networks, Internet of Things, big data analytics, privacy, optimization techniques and quality of service issues in wireless networks.
作者簡介(中文翻譯)
穆拉德·汗（Murad Khan）於2008年在巴基斯坦佩什瓦爾大學獲得計算機科學學士學位。他在韓國大邱的慶北國立大學計算機科學與工程學院完成了計算機科學與工程的博士學位。汗博士已發表超過50篇國際會議和期刊論文，並與Springer和CRC Press合著了兩本書的章節。他還擔任過多個知名國際會議的程序委員會成員，如ACM SAC 2017、ICFNDS 2017，並為多本期刊擔任審稿人，如《Future Generation Systems》（Elsevier）和《IEEE Access》。在2016年，他因設計智能家居控制系統而獲得慶北國立大學的高通創新獎。他還因在2015年西班牙薩拉曼卡的ACM SAC會議上對多準則切換技術的研究而獲得銅獎。他是多個社群的成員，包括ACM、IEEE和CRC Press。他的專業領域包括臨時網路和無線網路、物聯網的架構設計，以及智能城市和家庭的通信協議設計、大數據分析等。
比拉爾·詹（Bilal Jan）於2010年和2015年分別在意大利都靈理工大學控制與計算機工程系（DAUIN）獲得碩士和博士學位。他在多個知名期刊和會議上發表了幾篇論文。目前，他在巴基斯坦達拉·亞當·凱爾的FATA大學擔任助理教授及計算機科學系主任。他是多本領先期刊的審稿人。他的研究興趣包括GPU的通用編程、高性能計算、無線傳感器網路、物聯網（IoT）、深度學習和大數據。
哈利姆·法爾曼（Haleem Farman）於2008年在巴基斯坦伊斯蘭堡的國際伊斯蘭大學獲得碩士學位。他目前在巴基斯坦佩什瓦爾大學計算機科學系攻讀博士學位，並在巴基斯坦伊斯蘭學院計算機科學系擔任講師。他已在多個受尊敬的期刊和會議上發表/共同發表超過20篇研究論文。此外，他還擔任多本期刊的邀請審稿人，如Elsevier的《Sustainable Cities and Society》。他的研究領域包括無線傳感器網路、物聯網、大數據分析、隱私、優化技術以及無線網路中的服務質量問題。
