  Neural network training process takes long time when the size of training
data is huge, without the large set of training values the neural network is
unable to learn features. This dilemma between time and size of data is often
solved using fast GPUs, but we present a better solution for a subset of those
problems. To reduce the time for training a regression model using neural
network we introduce a loss function called Nth Absolute Root Mean Error
(NARME). It helps to train regression models much faster compared to other
existing loss functions. Experiments show that in most use cases NARME reduces
the required number of epochs to almost one-tenth of that required by other
commonly used loss functions, and also achieves great accuracy in the small
amount of time in which it was trained.
