  Deep learning Networks play a crucial role in the evolution of a vast number
of current machine learning models for solving a variety of real world
non-trivial tasks. Such networks use big data which is generally unlabeled
unsupervised and multi-layered requiring no form of supervision for training
and learning data and has been used to successfully build automatic supervisory
neural networks. However the question still remains how well the learned data
represents interestingness, and their effectiveness i.e. efficiency in deep
learning models or applications. If the output of a network of deep learning
models can be beamed unto a scene of observables, we could learn the
variational frequencies of these stacked networks in a parallel and
distributive way.This paper seeks to discover and represent interesting
patterns in an efficient and less complex way by incorporating the concept of
Mode synthesizers in the deep learning process models
