大数据深度学习应用综述

Rofia Abada, A. Abubakar, Muhammad Tayyab Bilal
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引用次数: 3

摘要

大数据是指企业、组织和个人每天产生的大量结构化和非结构化数据。深度学习是一种机器学习,涉及使用人工神经网络来学习数据中的模式和关系。本文讨论了深度学习在大数据分析领域的应用。我们概述了深度学习和大数据,然后深入研究了深度学习如何在各个领域中用于从大数据中提取价值的具体示例。这些领域包括预测分析、图像和视频分析、自然语言处理和推荐系统。我们还讨论了使用深度学习进行大数据分析的一些挑战和限制,以及该领域未来的研究和发展方向。总的来说,深度学习已经被证明是从大数据中提取见解的强大工具,并且可能在数据科学领域发挥越来越重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Overview on Deep Leaning Application of Big Data
Big data refers to the large volumes of structured and unstructured data that are generated by businesses, organizations, and individuals on a daily basis. Deep learning is a type of machine learning that involves the use of artificial neural networks to learn patterns and relationships in data. In this paper, we discuss the applications of deep learning in the field of big data analysis. We provide an overview of deep learning and big data, and then delve into specific examples of how deep learning has been used in various domains to extract value from big data. These domains include predictive analytics, image and video analysis, natural language processing, and recommendation systems. We also discuss some of the challenges and limitations of using deep learning for big data analysis, as well as future directions for research and development in this field. Overall, deep learning has proven to be a powerful tool for extracting insights from big data, and is likely to play an increasingly important role in the field of data science.
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