云中的深度学习神经网络

Burhan Humayun Awan
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引用次数: 0

摘要

深度神经网络(dnn)目前作为机器学习技术广泛应用于现实世界的关键应用中。由于构成深度神经网络的参数数量众多,学习和预测任务需要数百万次浮点运算(FLOPs)。在云计算系统中实现深度神经网络是一种更有效的策略,该云计算系统具有高速高性能计算能力的集中式服务器和数据存储子系统。本研究对云计算中使用的最新深度神经网络进行了更新分析。它强调了云计算的必要性,同时提出和讨论了与各种架构相关的许多DNN复杂性问题。此外,它还进入了它们的复杂性,并为DNN部署提供了几个云计算平台的全面分析。此外,它还检查了已经在云计算平台上运行的深度神经网络应用程序,以突出使用云计算用于深度神经网络的优势。该研究强调了在云计算系统中实施深度神经网络的困难,并为改进当前和未来的部署提供了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Neural Networks in the Cloud
Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.
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