Issue #37

Feb 9, 2020

Dear friends,

Welcome to the Oscar night edition of the Sunday Briefing.

This week we introduce Layer Cake, a new graphics framework for the web, a dataset aimed at deduplicating GitHub Repositories for further analysis, the latest European union Ethics guidelines for AI and Netflix's approaches to Data Compression for Large-Scale Streaming Experimentation

From the Ivory tower, we received a Tutorial on Learning With Bayesian Networks, approaches to solve large scale Knapsack Problems, and Facebook AI team's ideas on how to make data Radioactive so that we can easily tell if it has been used to train a specific model or not.

Finally, in the weeks video we have the first lecture in the MIT Introduction to Deep Learning course by Alexander Amini.

Data shows that the best way for a newsletter to grow is by word of mouth, so 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 spread the word!

Semper discentes,

The D4S team

Blog:

Our latest post covers section 1.2 Simpson's Paradox, a common yet poorly understood paradox that is common in Data Science. 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:
1.2 - Simpson's Paradox

GitHub: github.com/DataForScience/Causality

Top Links:

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

Layer Cake, a graphics framework for more flexible web graphics [flowingdata.com]
AI for AI: Metareasoning for modular computing systems [microsoft.com]
Ethics guidelines for trustworthy AI [ec.europa.eu]
Handling Big Datasets for Machine Learning [towardsdatascience.com]
Data Compression for Large-Scale Streaming Experimentation [netflixtechblog.com]
Time Series Cross-validation — a walk forward approach in python [medium.com/eatpredlove]

Fresh off the press:

Some of the most interesting academic papers published recently.

The role of worldviews in the governance of sustainable mobility (F. Chuang, E. Manley, A. Petersen)
On Geometry of Information Flow for Causal Inference (S. Surasinghe, E. M. Bollt)
Machine learning for asymmetric catalysis (J. Yeston)
A Tutorial on Learning With Bayesian Networks (D. Heckerman)
Solving Billion-Scale Knapsack Problems (X. Zhang, F. Qi, Z. Hua, S. Yang)
Radioactive data: tracing through training (A. Sablayrolles, M. Douze, C. Schmid, H. Jégou)
A Dataset for GitHub Repository Deduplication (D. Spinellis, Z. Kotti, A. Mockus)

Video of the week:

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

MIT Introduction to Deep Learning

https://www.youtube.com/watch?v=njKP3FqW3Sk

Upcoming Events:

Opportunities to learn from us

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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