  The success stories from deep learning models increase every day spanning
different tasks from image classification to natural language understanding.
With the increasing popularity of these models, scientists spend more and more
time finding the optimal parameters and best model architectures for their
tasks. In this paper, we focus on the ingredient that feeds these machines: the
data. We hypothesize that the data ordering affects how well a model performs.
To that end, we conduct experiments on an image classification task using
ImageNet dataset and show that some data orderings are better than others in
terms of obtaining higher classification accuracies. Experimental results show
that independent of model architecture, learning rate and batch size, ordering
of the data significantly affects the outcome. We show these findings using
different metrics: NDCG, accuracy @ 1 and accuracy @ 5. Our goal here is to
show that not only parameters and model architectures but also the data
ordering has a say in obtaining better results.
