Dear friends,

Welcome to the May 10 edition of the Sunday Briefing.

This week we continue our blog series on Epidemic Modeling with the fourth post of the series: "Epidemic Modeling 104: Impact of Seasonal effects on CoVID-19". You can also follow along with the GitHub repository containing the respective Python code. We hope you find it useful and gladly welcome any comments you might have.

The past week has been a good week for new software projects, with two interesting contributions coming across our desk: scikit-network, a Python package inspired by scikit learn to analyze large graphs and giotto-ai a topological machine learning toolbox. On the visualization side, we have a visual explanation for regularization of linear models and a brief introduction to the beauty of Information Theory.

From academia, the main developments have been an analysis of the mortality impact of the COVID19 restrictions, an overview of recent results on the Bitcoin Transaction Networks and a tutorial on Explainable Deep Learning.

Finally, in our video of the week, Vladimir Vapnik, one of the main contributors to Machine Learning in the past few decades guides us through a lecture on the Complete Statistical Theory of Learning.

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Semper discentes,

The D4S team