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.

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