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Deep learning Networks play a crucial role in the evolution of a vast numberof current machine learning models for solving a variety of real worldnon-trivial tasks. Such networks use big data which is generally unlabeledunsupervised and multi-layered requiring no form of supervision for trainingand learning data and has been used to successfully build automatic supervisoryneural networks. However the question still remains how well the learned datarepresents interestingness, and their effectiveness i.e. efficiency in deeplearning models or applications. If the output of a network of deep learningmodels can be beamed unto a scene of observables, we could learn thevariational frequencies of these stacked networks in a parallel anddistributive way.This paper seeks to discover and represent interestingpatterns in an efficient and less complex way by incorporating the concept ofMode synthesizers in the deep learning process models

Learning Representations from Deep Networks Using Mode Synthesizers