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Advancement Of Deep Learning In Big Data And Distributed Systems

机译：大数据和分布式系统深度学习的进步

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Digital computing space has grown dramatically since the beginning of the 2000s to deal with an increase in data proliferation. These come from a wide variety area. For example, the number of connected devices explodes with the advent of the Internet of Things. These machines generate a growing number of data, which must be analyzed, by their interactions with the outside environment and its various sensors. Social networks are also another field in which various data has been used, interactive data and metadata that provide information on user profiles. All these data require the storage of large capacity and analysis of several data. In effect, if the arrival of this quantities of information demanded storage improvement, significant advances in processing and interpretation were also required and feasible. In this paper, the main contributions are summarized in a comparison table as detailed in Table 1, like the objectives, challenges, and novelty of each paper are clarified. The architecture or model and applications used— finally, the recommendations for each.
机译：自2000年代开始以来，数码计算空间已经大幅增长，以处理数据增殖的增加。这些来自各种各样的地区。例如，连接设备的数量随着物联网的出现而爆炸。这些机器通过与外部环境的交互及其各种传感器产生越来越多的数据，必须分析越来越多的数据。社交网络也是另一个领域，其中已经使用了各种数据，交互式数据和提供了关于用户配置文件信息的元数据。所有这些数据都需要存储大容量和几个数据的分析。实际上，如果这笔数量信息所需的储存改善，还需要加工和解释的显着进展也是可行的。在本文中，主要贡献总结在比较表中，详细说明了每篇论文的目标，挑战和新颖性。使用的架构或模型和应用程序 - 最后，每个建议。

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