Distributed Deep Belief network
DBNs have so far achieved a lot in numerous applications such as speech and phone recognition [127], information retrieval [128], human motion modelling[129], and so on. However, the sequential implementation for both RBM and DBNs come with various limitations. With a large-scale dataset, the models show various shortcomings in their applications due to the long, time consuming computation involved, memory demanding nature of the algorithms, and so on. To work with Big data, RBMs and DBNs require distributed computing to provide scalable, coherent and efficient learning.
To make DBNs acquiescent to the large-scale dataset stored on a cluster of computers, DBNs should acquire a distributed learning approach with Hadoop and Map-Reduce. The paper in [130] has shown a key-value pair approach for each level of an RBM, where the pre-training is accomplished with layer-wise, in a distributed environment in Map-Reduce framework. The learning is performed on Hadoop...
