Dear friends,

Welcome to the Easter Sunday edition of the Sunday Briefing.

Last week we continued our look at epidemic modeling strategies with our second blog post in the Epidemic Modeling series: Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful. If you haven't had a chance to check it out yet, you should. This week we took a break from blogging but keep an eye out for new posts in the near future. As always you can follow along with a GitHub repository containing the respective Python code. We hope you find it useful and gladly welcome any comments you might have.

In our regularly scheduled content we take a look on Mathematical Notation and Communication in Machine Learning, how to process PDF forms with Python and why pie charts often suck. 

On the academic front, we have an exploration of the unreasonable effectiveness of deep learning, the efforts to Integrate Deep Learning and Neuroscience and A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models 

Finally, on our video of the week, Rob Chew and Peter Baumgartner guide us through A Social Network Analysis Tutorial with NetworkX, a topic that is near and dear to our hearts but often overlooked by data scientists.

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Semper discentes,

The D4S team