Evaluation of neural network running on Spark

Pao Yue-kong Library Electronic Theses Database

Evaluation of neural network running on Spark

 

Author: Fan, Jianing
Title: Evaluation of neural network running on Spark
Degree: M.Sc.
Year: 2015
Subject: Neural networks (Computer science)
Back propagation (Artificial intelligence)
Machine learning
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Computing
Pages: viii, 84 leaves : illustrations ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2780025
URI: http://theses.lib.polyu.edu.hk/handle/200/7845
Abstract: Big data is an unstoppable trend in the era of information, which opens up a new door to understand the world and make decision. Digging up useful knowledge and information from massive data is applied by artificial intelligent technologies such as machine learning, data mining and natural language processing. As the main tool for analysis of big data, machine learning speeds up the development of big data. Neural network algorithm is a type of machine learning algorithm for classification and pattern recognition. Among a variety of neural network algorithms proposed, backpropagation neural network is the most widely used so far. Apache Spark is a young big data analytical platform like Hadoop. It provides users with not only amazing performance but also easy-to-use programming interface and powerful machine learning library. No formal study has assessed the performance of neural network algorithm running on Spark platform so far. Accordingly, we evaluate Spark by implementing backpropagation neural network algorithm on it.

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