ABSTRACT
In this chapter, we put an effort to focus on the processing and storage issues that can be carried out from the updates of unstructured data in the unconstraint environment. In the technological aspects, we discuss the existing intelligent deep network architectures along with traditional machine learning approaches. The outcomes of the analytical discussion presented in this chapter enable to picturize a generalized intelligent neural network for processing highly complex visual data with graphs and manifold structures. The essential issues with updating the hidden layers and some fast optimization techniques are also introduced. Finally, this can be concluded that the presented work reflects the sounding challenges to process and extract qualitative information from the densely unstructured tremendous amount of training data. In case of video processing, we need to frame out various deep learning aspects that can lead the research work to highly resourceful scope for deep data analytics and many problems that require high-performance computing in visual media.
