Back Propagated Neural Network on MapReduce Frameworks: A Survey
Back propagated Neural Network (BPNN) is a popular supervised learning technique utilized in machine learning and deep learning to extract knowledge. However, BPNN has shown poor time and space complexity with large volume of data due to its in memory processing. Huge amount of data sourced by ample of applications and services on the Internet needs to be distributed and parallel environment like MapReduce framework for processing and storage. The large volume of data is divided into smaller chunks and MapReduce framework exploits the cluster of commodity hardware to process these chunks. This research focuses on various MapReduce based BPNN algorithms such as single pass and multi pass MapReduce based BPNN. The research is intended to perform in depth comparisons of these techniques considering various parameters such as computation complexity of Mapper and Reducer, granularity of Mapper's output, number of MapReduce jobs required to build BPNN, and Mapper's pairs.