Deep Learning is a computational heavy process. Cutting down the costs is one major challenge along with data curation. Their power hungry training processes had garnered such reputation that researchers have published works reporting the carbon footprint of training networks. As things can only get complicated from here on, heading into a future with a deluge of machine learning applications, we see new strategies being invented to make training neural networks as efficient as problem. Updating a neural network to change its predictions on a single input can decrease performance across other inputs. While being simple, this approach is not robust to minor changes in the input.