Deep Learning and Big Data are today two very active research areas in all computer science research domains. Learning techniques specifically convey crucial opportunities and transformative potential in several areas of thinking and innovative learning methods to address a number of issues. Deep Learning successes is in a broad area of applications such as speech recognition, computer vision, and natural language processing. Big Data often possesses a large number of examples, large varieties of class types, and very high dimensionality. High volumes of data present a great challenging issue for Deep Learning. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process.
The aim of this paper is to discuss the recent advancement and application of Deep Learning in Big Data, to analyze the Deep Learning tools, associated challenges, open research problems and the solutions proposed.
Deep Learning can be utilized for addressing some important problems in Big Data.
We conclude this paper, that Deep Learning has an advantage of potentially providing a solution to address the data analysis and learning problems found in massive volumes of input data. Deep Learning attempts to model various levels of abstraction within data.