大数据与深度学习综述

Deep learning with big data:state of the art and development

大数据时代改变了基于数理统计的传统数据科学,促进了数据分析方法的创新,从机器学习和多层神经网络演化而来的深度学习是当前大数据处理与分析的研究前沿。从机器学习到深度学习,经历了早期的符号归纳机器学习、统计机器学习、神经网络和20世纪末开始的数据挖掘等几十年的研究和实践,发现深度学习可以挖掘大数据的潜在价值。本文给出大数据和深度学习的综述,特别是,给出了各种深层结构及其学习算法之间关联的图谱,给出了深度学习在若干领域应用的知名案例。最后,展望了大数据上深度学习的发展与挑战。

As the era of the big data arrives, it is accompanied by profound changes to traditional data science based on statistics. Big data also pushes innovations in the methods of data analysis. Deep learning that evolves from machine learning and muhilayer neural networks are currently extremely active research areas. From the symbolic machine learning and statistical machine learning to the artificial neural network, followed by data mining in the 90s, this has built a solid foundation for deep learning （DL） that makes it a notable tool for discovering the potential value behind big data. This survey compactly summarized big data and DL, proposed a generative relationship tree of the major deep networks and the algorithms, illustrated a broad area of applications based on DL, and highlighted the challenges to DL with big data, as well as identified future trends.