An Analysis of Integrate Large Scale Deep Learning using Mobile Big Data

Computer Engineering

Deep learning methods have been applied extensively to learning methods in various fields of science and engineering such as speech recognition, image classification and language processing. Similarly, traditional data processing techniques have several limitations in processing large amounts of data. Furthermore, Big Data Analytics requires new and sophisticated algorithms based on machine and deep learning techniques to process the data in real time with high accuracy and efficiency. However, recently, research has included various intensive learning techniques with training mechanisms of hybrid learning and processing data with high speed. Most of these techniques are specific to scenarios and thus are based on vector space, reflecting poor performance in common scenarios and learning characteristics in big data. Furthermore, one of the reasons for such failure is the high involvement of humans to design sophisticated and optimized algorithms based on machine and intensive learning techniques. This paper describes a development environment integrating big data architecture and deep learning models to facilitate rapid experimentation. This paper makes three major contributions: first, it describes a big data architecture supporting an organization that supports large data collection and deep learning models and, second, it is used to build the data visualization described. The language used is converting different large data streams into one. Single view that is used by a deep learning system. Third, it has demonstrated the effectiveness of the system by applying the tool to many different deep learning applications.

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