Wavelet neural network for big data analytics in banking via GPU

Big data is hard to process using conventional technologies and hence calls for massively parallel processing. Machine learning techniques have been widely adopted in several massive and complex data-intensive fields for handling large data. Artificial neural networks (ANNs) are the most common machine learning techniques used for classification, function approximation, dimensionality reduction etc. Wavelet neural network (WNN) is one of them. The architecture of WNN is having a lot of matrix computations, which can be parallelized by GPU. Theano is used as a programming model for accelerating general-purpose workloads. In our work, we implemented the WNN using Theano. The efficacy of WNN is tested on various bank datasets. In this process, the performance of a conventional CPU implementation of WNN was tested with that of GPU, and the latter was found to be much faster on all datasets.