標(biāo)題: Titlebook: Distributed Machine Learning and Gradient Optimization; Jiawei Jiang,Bin Cui,Ce Zhang Book 2022 The Editor(s) (if applicable) and The Auth [打印本頁] 作者: Nutraceutical 時間: 2025-3-21 17:15
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
書目名稱Distributed Machine Learning and Gradient Optimization年度引用
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
作者: Fibroid 時間: 2025-3-21 23:06
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 作者: 繁榮地區(qū) 時間: 2025-3-22 04:21 作者: Anthem 時間: 2025-3-22 05:14 作者: 步履蹣跚 時間: 2025-3-22 09:42
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.作者: irreparable 時間: 2025-3-22 14:42 作者: irreparable 時間: 2025-3-22 20:42 作者: Gerontology 時間: 2025-3-22 22:23 作者: CHOIR 時間: 2025-3-23 03:00
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.作者: 商業(yè)上 時間: 2025-3-23 07:01
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作者: 宏偉 時間: 2025-3-23 09:54 作者: FRAX-tool 時間: 2025-3-23 17:36 作者: Contend 時間: 2025-3-23 20:50 作者: 人工制品 時間: 2025-3-24 01:09 作者: Relinquish 時間: 2025-3-24 02:43 作者: 控訴 時間: 2025-3-24 09:42 作者: Solace 時間: 2025-3-24 14:40
Distributed Gradient Optimization Algorithms,We classify these methods by their targeted machine learning models, including but not limited to generalized linear models, deep learning models, and tree models. For each category of machine learning model, we briefly describe their concepts and principles and then introduce the existing gradient optimization algorithms that can be adopted.作者: 支形吊燈 時間: 2025-3-24 17:35 作者: 無辜 時間: 2025-3-24 23:03 作者: 上流社會 時間: 2025-3-25 00:40 作者: NICHE 時間: 2025-3-25 04:24
https://doi.org/10.1057/9780230253025We classify these methods by their targeted machine learning models, including but not limited to generalized linear models, deep learning models, and tree models. For each category of machine learning model, we briefly describe their concepts and principles and then introduce the existing gradient 作者: 甜食 時間: 2025-3-25 09:18 作者: 相容 時間: 2025-3-25 12:28 作者: Cultivate 時間: 2025-3-25 19:00 作者: 外露 時間: 2025-3-25 20:20
978-981-16-3422-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: landmark 時間: 2025-3-26 04:07
Distributed Machine Learning and Gradient Optimization978-981-16-3420-8Series ISSN 2522-0179 Series E-ISSN 2522-0187 作者: 取之不竭 時間: 2025-3-26 08:06
https://doi.org/10.1057/9780230253025We classify these methods by their targeted machine learning models, including but not limited to generalized linear models, deep learning models, and tree models. For each category of machine learning model, we briefly describe their concepts and principles and then introduce the existing gradient optimization algorithms that can be adopted.作者: 背帶 時間: 2025-3-26 10:26 作者: 合法 時間: 2025-3-26 14:52
Jiawei Jiang,Bin Cui,Ce ZhangPresents a comprehensive overview of distributed machine learning.Introduces the progress of gradient optimization for distributed machine learning.Addresses the key challenge of implementing machine 作者: subordinate 時間: 2025-3-26 17:35
Big Data Managementhttp://image.papertrans.cn/e/image/281918.jpg作者: concise 時間: 2025-3-27 00:28
9樓作者: Restenosis 時間: 2025-3-27 04:23
9樓作者: Ordeal 時間: 2025-3-27 05:50
10樓作者: 橫截,橫斷 時間: 2025-3-27 12:26
10樓作者: Mosaic 時間: 2025-3-27 14:07
10樓作者: 單片眼鏡 時間: 2025-3-27 19:28
10樓
