Impact of Deep Learning in Big Data Analytics

Abstract

New technologies enable us to collect more data than ever before. With an overwhelming amount of web-based, mobile, and sensor-generated data arriving at a terabyte and even zeta byte scale, new science and insights can be discovered from the highly detailed and domain-specific information which can contain useful information about problems such as national intelligence, cyber security, fraud detection, financial trading, personalized medicine and treatments, personalized information and recommendations and personalized athletic training. Machine learning algorithms, particularly deep learning (evolved from artificial neural networks) plays a vital role in big data analysis. Deep Learning algorithms extracts high-level and complex abstractions by discovering intricate structure in large data sets. Deep learning techniques are nowadays the leading approaches to solve complex machine learning and pattern recognition problems such as speech and image understanding, semantic indexing, data tagging and fast information retrieval. This paper focuses on all aspects of big data analytics, with a particular emphasis on the analysis and learning of massive volume of unstructured data and developing effective and efficient large-scale learning algorithms.