Many talk about data science and machine learning with enthusiasm, but few know about one of the most important building components behind them – convex optimization. Indeed, nowadays nearly every data science problem will first be transformed into an optimization problem and then solved by standard methods. Convex optimization, albeit basic, is the most important concept in optimization and the starting point of all understanding. If you are an aspiring data scientist, convex optimization is an unavoidable subject that you had better learn sooner than later.

Many newcomers of the Julia language feel confused about the value type described in the official documentation. They don’t understand what it is used for and why other languages don’t have this feature. In fact, as a “secret” rarely shared by the core developers, you may probably never need the value type.

In this post I will discuss one of the two best papers in ICML 2018 – Delayed Impact of Fair Machine Learning. Contrary to other papers constructing various innovative definitions of fairness, this paper analyzes the delayed impact of fairness policy. It shows that these policies do not necessarily improve the situation of the disadvantaged population: It may hurt them, in some cases, in the long run.

TL;DR: Evolutionary game theory, initially developed for biology, has been successfully applied to other domains such as economics, sociology, and anthropology. This post will use it to explain the various meta of Yugioh Duel Links and predict the nonexistence of the kind of meta which is simultaneously diverse, accessible, and without being Rock–paper–scissors.

Image from Gustavo Santos at DeviantArt