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Issue #38

Feb 16, 2020

Dear friends,

Welcome to the Feb 16th edition of the Sunday Briefing.

This week was a major week for major project developments, with Facebook announcing the release of an Unprecedented URL Dataset that they are making available for academic research, Microsoft publishing Turing-NLG a new state of the art language model, fast.ai releasing their new API for Deep Learning  and SciPy celebrating their 1.0 release with a overview article in Nature.

Speaking of articles, this week we also have an overview of Machine Learning in Python by Sebastian Raschka, an introduction to Hypergraphs and Metrics for graph comparison and the latest developments on the how to predict the speed Epidemic Spreading in Networks, a topic near and dear to our hearts.

Finally, in the video of the week, Greg Winther will give us a timely overview on the basics of modeling epidemic spreading by Implementing a SIR Disease Model in Python.

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.
  1. Unprecedented Facebook URLs Dataset now Available for Academic Research [socialscience.one]
  2. Understanding Causal Inference [blog.dominodatalab.com]
  3. Quantifying Independently Reproducible Machine Learning [thegradient.pub]
  4. Turing-NLG: A 17-billion-parameter language model by Microsoft [microsoft.com]
  5. Towards a general theory of "adversarial examples," the bizarre, hallucinatory motes in machine learning's all-seeing eye [boingboing.net]
  6. Data Science, AI and Hype cycles [finextra.com]
  7. Using Dynamic Time Warping and MLflow to Detect Sales Trends [databricks.com]
  8. Geolocation and Geocodes in Python [medium.com/better-programming]

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.


Implementing a SIR Disease Model in Python

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

Upcoming Events:

Opportunities to learn from us
  1. Feb 28, 2020Graphs and Network Algorithms for Everyone [Register]
  2. Mar 9, 2020Data Visualization with matplotlib and seaborn [Register]  🆕
  3. Mar 15-16, 2020 - Time series modeling: ML and deep learning approaches - Strata/AI [Register
  4. Mar 27, 2020Deep Learning for Everyone [Register] 🆕
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