Secure protocol for implementation of deep learning in big data domains

Abstract

Deep learning is used widely in Big Data to grab the most beneficial results. Big Data and Deep learning are the two hot trends which gained the focus of enormous researchers nowadays. Due to different kind of challenges faced, to train deep learning algorithms with big data and deploy the deep learning algorithms in the context of big data to achieve the expected out comes, different types of challenges are specified in this research work. Challenges like enormous volume of data, different varieties of data that, the different variability of data and many more are specified in this research work and these have different impacts on the deep learning algorithms. Deep learning could be a set or technique capable of learning an unattended type of information that is unstructured or untagged information. Whereas Big Data could be an information contained both structured and unstructured data. The primary purpose of the conducted literature review or the research is to identify different challenges or issues to adoption of deep learning in situation of Big Data, various challenges have been recognized. Here in this work we developed a secure protocol for the mentioned objective. The expected effect of this assessment will be to in gradient challenges to implementation of deep learning in big data in various domains.