Issue #36

Feb 2, 2020

Dear friends,

Welcome to the Super Bowl Sunday and Palindrome day edition of the Sunday Briefing.

This week we take a look at Probability and ML Research, introduce a new causal inference library from Microsoft and an introduction to Bag of Words from FreeCodeCamp. On the academic side, we look at The Case for Bayesian Deep Learning and the Benefits of Capsule Neural Networks.

Today we are happy to announce the first blog post covering Judea Pearls's Causal Inference in Statistics - A Primer (affiliate link) a short and to the point introduction to Causality. We invite you to follow along and send use your comments and suggestions. Naturally, having a physical copy of the book is not a requirement, but it's highly recommended as it makes it easier to follow along.

Finally, Michael Galarnyk guides us through Heatmaps using Matplotlib, Seaborn, and Pandas in our video of the week.

If you think one of your friends or colleagues would enjoy this newsletter, just go ahead and forward this email to them and help us grow!

Semper discentes,

The D4S team

Today's post covers section 1.2 Simpson's Paradox, a common yet poorly understood paradox that is common in Data Science. We're also releasing the GitHub repository we will be using to host the code and examples for this project: github.com/DataForScience/Causality

Blog Posts:
1.2 - Simpson's Paradox

GitHub: github.com/DataForScience/Causality

Top Links:

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

DoWhy | Making causal inference easy [Microsoft]
An Opinionated Guide to ML Research [joschu.net]
Some Useful Probability Facts for Systems Programming [theartofmachinery.com]
Climbing the ladder of causality [michielstock.github.io]
All algorithms implemented in Python (for education) [github.com/TheAlgorithms]
Leveraging Elastic Demand for Forecasting [tech.instacart.com]
New perspectives on contextual bandit [microsoft.com]
An introduction to Bag of Words and how to code it in Python for NLP [FreeCodeCamp]

Fresh off the press:

Some of the most interesting academic papers published recently.

Modeling Echo Chambers and Polarization Dynamics in Social Networks (F. Baumann, P. Lorenz-Spreen, I. M. Sokolov, M. Starnini)
The Case for Bayesian Deep Learning (A. G. Wilson)
Feature selection in machine learning: Rényi min-entropy vs Shannon entropy (C. Palamidessi, M. Romanelli)
Interventions for Ranking in the Presence of Implicit Bias (L. E. Celis, A. Mehrotra, N. K. Vishnoi)
stream-learn -- open-source Python library for difficult data stream batch analysis (P. Ksieniewicz, P. Zyblewski)
Examining the Benefits of Capsule Neural Networks (A. Punjabi, J. Schmid, A. K. Katsaggelos)

Video of the week:

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

Heatmaps using Matplotlib, Seaborn, and Pandas

https://www.youtube.com/watch?v=m7uXFyPN2Sk

Upcoming Events:

Opportunities to learn from us

Feb 10, 2020 - Natural Language Processing (NLP) for Everyone[Register]
Feb 28, 2020 - Graphs and Network Algorithms for Everyone [Register]
Mar 9, 2020 - Data Visualization with matplotlib and seaborn [Register] 🆕
Mar 15-16, 2020 - Time series modeling: ML and deep learning approaches - Strata/AI [Register]
Mar 27, 2020 - Deep Learning for Everyone [Register] 🆕

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