~brilee

An overview of how the NumPy family of libraries help make Python more performant for scientific computing, without losing the benefits Python brings. — nicholasbs

Brian gives a deep explanation of how Monte Carlo Tree Search (a key technique for game AIs) works. He implemented the AlphaGo paper from scratch, so he should know! — porterjamesj

An explanation of metrics for comparing statistical classifiers, and why the most common one, AUROC, is so commonly used. — porterjamesj

A minimal Go AI modeled on AlphaGo. It became the basis of Minigo (https://github.com/tensorflow/minigo), which is maintained by the Tensorflow team. — davidbalbert