  High volume of data, perceived as either challenge or opportunity. Deep
learning architecture demands high volume of data to effectively back propagate
and train the weights without bias. At the same time, large volume of data
demands higher capacity of the machine where it could be executed seamlessly.
Budding data scientist along with many research professionals face frequent
disconnection issue with cloud computing framework (working without dedicated
connection) due to free subscription to the platform. Similar issues also
visible while working on local computer where computer may run out of resource
or power sometimes and researcher has to start training the models all over
again. In this paper, we intend to provide a way to resolve this issue and
progressively training the neural network even after having frequent
disconnection or resource outage without loosing much of the progress
