D4S Sunday Briefing #50

Original article was published on Deep Learning on Medium

May 10, 2020

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

Blog:

Our latest blog post in the Epidemic Modeling series introduces seasonality into our formulation of Epidemic Models and how it can be used to explore how the changes in weather might impact the current CoVID-19 pandemic. GitHub: github.com/DataForScience/Epidemiology101

The latest post in the Causality series covers the first part of section 1.3 Probability Theory and Statistics, an overview of some of the fundamental theoretical requirements for the journey ahead. The code for each blog post in this series is hosted by a dedicated GitHub repository for this project: github.com/DataForScience/Causality

Blog Posts:
Epidemic Modeling:
Epidemic Modeling 101: Or why your CoVID-19 exponential fits are wrong
Epidemic Modeling 102: All CoVID-19 models are wrong, but some are useful
Epidemic Modeling 103: Adding confidence intervals and stochastic effects to your CoVID-19 Models
Epidemic Modeling 104: Impact of Seasonal effects on CoVID-19

Causality:
1.2 — Simpson’s Paradox
1.3 — Probability Theory and Statistics

GitHub: github.com/DataForScience/Causality

Top Links:

Tutorials and blog posts that came across our desk this week.

  1. Covering science at dangerous speeds [cjr.org]
  2. Google’s medical AI was super accurate in a lab. Real life was a different story [technologyreview.com]
  3. A high-performance topological machine learning toolbox in Python [github.com/giotto-ai]
  4. scikit-network: Python package for the analysis of large graphs [scikit-network.readthedocs.io]
  5. Seasonality of SARS-CoV-2: Will COVID-19 go away on its own in warmer weather? [ccdd.hsph.harvard.edu]
  6. A visual explanation for regularization of linear models [explained.ai]
  7. A foolproof way to shrink deep learning models [news.mit.edu]
  8. A brief introduction to the beauty of Information Theory [notamonadtutorial.com]

Fresh off the press:

Some of the most interesting academic papers published recently.

Video of the week:

Interesting discussions, ideas or tutorials that came across our desk.

Complete Statistical Theory of Learning

Upcoming Events:

Opportunities to learn from us

  1. May 18, 2020 — Graphs and Network Algorithms for Everyone [Register]
  2. Jun 1, 2020 — Data Visualization with matplotlib and seaborn for Everyone [Register] 🆕
  3. Jun 17, 2020 — Deep Learning for Everyone [Register] 🆕

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