理解大数据的概念和当前场景

Chirag Kumar Dilipbhai Patel i Patel, D. Peddi
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引用次数: 0

摘要

大数据分析和深度学习是数据科学最有前途的两个融合领域。大数据的重要性最近有所增长,因为一些公共和商业组织已经积累了大量的特定区域数据,这些数据可能为国家信息、高级安全、勒索领域、发展和繁荣信息等主题提供有用的信息。对于大数据分析来说,数据通常是非结构化和未标记的,深度学习本身分析和学习大量数据的能力是一个至关重要的功能。在这篇综述中,我们将探讨如何使用深度学习来解决大数据分析中一些最紧迫的问题,包括从大型数据集中分离模型、语义查询、数据标记、智能数据恢复以及判别任务的自动化。
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Understand the Idea of Big Data and in Present Scenario
Big data analytics and deep learning are two of data science's most promising areas of convergence. The importance of Big Data has grown recently as several organizations, both public and commercial, have been amassing large amounts of region-specific data that may provide useful information on topics like as national information, advanced security, blackmail area, development, and prosperity informatics. For Big Data Analytics, where data is often unstructured and unlabeled, Deep Learning's ability to analyze and learn from large amounts of data on its own is a crucial feature. In this review, we look at how Deep Learning can be used to solve some of the most pressing problems in Big Data Analytics, including model isolation from large data sets, semantic querying, data marking, smart data recovery, and the automation of discriminative tasks.
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