We care about understanding the machine learning models we produce and making them as small and accurate as possible. We like to give our understanding back to the community by explaining techniques we’ve developed on tiny datasets.
The open source notebooks that form part of this series officially held the #1 spots of two Stanford machine learning league tables for over six months until April 2019. Today, all models that currently rank above those notebooks are derivations of this original work.
In the final post of the series we come full circle, speeding up our single-GPU training implementation to take on a field of multi-GPU competitors. We roll-out a bag of standard and not-so-standard tricks to reduce training time to 34s, or 26s with test-time augmentation.