Titlebook: Distributed Machine Learning and Gradient Optimization; Jiawei Jiang,Bin Cui,Ce Zhang Book 2022 The Editor(s) (if applicable) and The Auth

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書目名稱Distributed Machine Learning and Gradient Optimization影響因子(影響力)




書目名稱Distributed Machine Learning and Gradient Optimization影響因子(影響力)學(xué)科排名




書目名稱Distributed Machine Learning and Gradient Optimization網(wǎng)絡(luò)公開度




書目名稱Distributed Machine Learning and Gradient Optimization網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Distributed Machine Learning and Gradient Optimization被引頻次




書目名稱Distributed Machine Learning and Gradient Optimization被引頻次學(xué)科排名




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書目名稱Distributed Machine Learning and Gradient Optimization年度引用學(xué)科排名




書目名稱Distributed Machine Learning and Gradient Optimization讀者反饋




書目名稱Distributed Machine Learning and Gradient Optimization讀者反饋學(xué)科排名

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Basics of Distributed Machine Learning,al techniques are involved in meeting the characteristics of distributed environments. In this chapter, we first conduct an anatomy of distributed machine learning, with which we understand the indispensable building blocks in designing distributed gradient optimization algorithms. Then, we provide

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Anthem

步履蹣跚

Conclusion,t fit the model parameters over the training data. As the data volume becomes larger and larger, extending gradient optimization algorithms to distributed environments is indispensable. This book thereby studies gradient optimization in the setting of distributed machine learning.

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Gerontology

CHOIR

Distributed Machine Learning Systems,t underlying infrastructures, e.g., new hardware (GPU/FPGA/RDMA), cloud environment, and databases. In this chapter, we will describe a broad range of machine learning systems in terms of motivations, architectures, functionalities, pros, and cons.

商業(yè)上

Book 2022ra, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Fo