Big Data Deep Learning: Challenges and Perspectives

TLDR

Deep learning is a highly active research area in machine learning and pattern recognition, achieving major successes in speech recognition, computer vision, and natural language processing, while big data offers transformative potential but also unprecedented challenges for harnessing data and information. The paper aims to provide a brief overview of deep learning and highlight current research efforts and challenges to big data, as well as future trends. The authors review existing literature to synthesize these insights. The paper summarizes the successes of deep learning across various applications and discusses the opportunities and challenges posed by big data.

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.