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Edge Learning for Distributed Big Data Analytics: Theory, Algorithms, and System Design

By (author) Song Guo, By (author) Zhihao Qu

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Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.

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Helgoland: The Strange and Beautiful Story of Quantum Physics

Carlo Rovelli
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208 pages
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The Coming Wave: AI, Power and Our Future

Mustafa Suleyman
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352 pages
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The Order of Time

Carlo Rovelli
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224 pages
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Originally published in Italian: L'ordine del tempo (Milan: Adelphi Edizioni, 2017).

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