• DocumentCode
    9132
  • Title

    Big Data Deep Learning: Challenges and Perspectives

  • Author

    Xue-wen Chen ; Xiaotong Lin

  • Author_Institution
    Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA
  • Volume
    2
  • fYear
    2014
  • fDate
    2014
  • Firstpage
    514
  • Lastpage
    525
  • Abstract
    Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.
  • Keywords
    Big Data; data analysis; learning (artificial intelligence); Big Data; deep learning; machine learning; pattern recognition; predictive analytics solutions; Big data; Data processing; Information analysis; Machine learning; Natural language processing; Pattern recognition; Classifier design and evaluation; feature representation; machine learning; neural nets models; parallel processing;
  • fLanguage
    English
  • Journal_Title
    Access, IEEE
  • Publisher
    ieee
  • ISSN
    2169-3536
  • Type

    jour

  • DOI
    10.1109/ACCESS.2014.2325029
  • Filename
    6817512