This unique text helps make sense of big data using signal processing techniques, in applications including machine learning, networking, and energy systems.
This unique text helps make sense of big data using signal processing techniques, in applications including machine learning, networking, and energy systems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Zhu Han is a Professor in the Department of Electrical and Computer Engineering, University of Houston, and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has co-authored several books, including Wireless Device-to-Device Communications and Networks (with Lingyang Song, Dusit Niyato and Ekram Hossain, Cambridge, 2015) and Game Theory in Wireless and Communication Networks (with Dusit Niyato, Walid Saad, Tamer Bäer and Are Hjørungnes, Cambridge, 2011).
Inhaltsangabe
Part I. Overview of Big Data Applications: 1. Introduction 2. Data parallelism: the supporting architecture Part II. Methodology and Mathematical Background: 3. First order methods 4. Sparse optimization 5. Sublinear algorithms 6. Tensor for big data 7. Deep learning and applications Part III. Big Data Applications: 8. Compressive sensing based big data analysis 9. Distributed large-scale optimization 10. Optimization of finite sums 11. Big data optimization for communication networks 12. Big data optimization for smart grid systems 13. Processing large data set in MapReduce 14. Massive data collection using wireless sensor networks.
Part I. Overview of Big Data Applications: 1. Introduction 2. Data parallelism: the supporting architecture Part II. Methodology and Mathematical Background: 3. First order methods 4. Sparse optimization 5. Sublinear algorithms 6. Tensor for big data 7. Deep learning and applications Part III. Big Data Applications: 8. Compressive sensing based big data analysis 9. Distributed large-scale optimization 10. Optimization of finite sums 11. Big data optimization for communication networks 12. Big data optimization for smart grid systems 13. Processing large data set in MapReduce 14. Massive data collection using wireless sensor networks.
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