Big Data Deep Learning: Challenges and Perspectives

• Published in: IEEE access Vol. 2; pp. 514 - 525
• Main Authors: Chen, Xue-Wen, Lin, Xiaotong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Big Data; Classifier design and evaluation; Computer vision; Data processing; Deep learning; feature representation; Information analysis; Machine learning; Natural language processing; neural nets models; parallel processing; Pattern recognition; Speech recognition
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2014.2325029

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.