Distributed deep learning and Hadoop
From the earlier sections of this chapter, we already have enough insights on why and how the relationship of deep learning and big data can bring major changes to the research community. Also, a centralized system is not going to help this relationship substantially with the course of time. Hence, distribution of the deep learning network across multiple servers has become the primary goal of the current deep learning practitioners. However, dealing with big data in a distributed environment is always associated with several challenges. Most of those are explained in-depth in the previous section. These include dealing with higher dimensional data, data with too many features, amount of memory available to store, processing the massive Big datasets, and so on. Moreover, Big datasets have a high computational resource demand on CPU and memory time. So, the reduction of processing time has become an extremely significant criterion. The following are the...
