Large-scale Artificial Neural Network: MapReduce-based Deep Learning
This work addresses scalability and efficiency issues in deep learning for large-scale data processing, though it appears incremental as it applies existing methods (MapReduce) to enhance neural network training.
The paper tackles the challenge of training deep learning models on large-scale data by combining deep learning with MapReduce on a cloud computing platform, resulting in a designed handwriting character recognizer that demonstrates improved efficiency in training and processing large datasets.
Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload. This project is mainly focused on the solution to the issues above, combining deep learning algorithm with cloud computing platform to deal with large-scale data. A MapReduce-based handwriting character recognizer will be designed in this project to verify the efficiency improvement this mechanism will achieve on training and practical large-scale data. Careful discussion and experiment will be developed to illustrate how deep learning algorithm works to train handwritten digits data, how MapReduce is implemented on deep learning neural network, and why this combination accelerates computation. Besides performance, the scalability and robustness will be mentioned in this report as well. Our system comes with two demonstration software that visually illustrates our handwritten digit recognition/encoding application.