D4S Sunday Briefing #47

Original article can be found here (source): Deep Learning on Medium

D4S Sunday Briefing #47

A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.

Issue #47

Apr 19, 2020

Dear friends,

Welcome to the 47th issue of the Sunday Briefing.

We are happy to announce we had a nice discussion with Ben Lorica from the Data Exchange Podcast about our recent blog series on Epidemic Modeling: Computational Models and Simulations of Epidemic Infectious Diseases. We are currently working on a couple of new posts that we’ll be published 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.

This week we continue our exploration of all things data science and machine learning. We take a deep dive into the intricacies of Forecasting s-curves (a topic that has become fashionable thanks to the current pandemic), a look at a relatively unknown Hungarian Statistician that created some visualization masterpieces, an in depth look at Interpretability by Fast Forward Labs and how to Monitor Machine Learning Models in Production

On the academic front, we have the latest paper by Yoshua Bengios’s group about a Trustworthy AI Development, a look at how Artificial intelligence is impacting clinicians, an In-depth Walkthrough on Evolution of Neural Machine Translation, and an interesting discussion on the dangers posed by misinformation and how Social-media companies must deal with it.

Finally, on our video of the week, the good folks at Jane Street give us A Taste of GPU Compute. A power series of techniques that are often misunderstood and unduly ignored.

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


Our latest blog post in the Epidemic Modeling series covers the power and limitations of Epidemic Models and how it can be used to understand 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

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. Forecasting s-curves is hard [constancecrozier.com]
  2. Event-Reduce — An algorithm to optimize database queries that run multiple times [github.com/pubkey]
  3. The Hungarian Statistician Behind Three Volumes of Visualization Masterpieces [medium.com/nightingale]
  4. Interpretability [fastforwardlabs.com]
  5. How to know if artificial intelligence is about to destroy civilization [technologyreview.com]
  6. Building a Social Network from the News using Graph Theory [towardsdatascience.com]
  7. Monitoring Machine Learning Models in Production [christophergs.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.

A Taste of GPU Compute

Upcoming Events:

Opportunities to learn from us

  1. Apr 29, 2020 — Applied Probability Theory for Everyone [Register]
  2. May 7, 2020 — Natural Language Processing (NLP) for Everyone [Register] 🆕
  3. May 18, 2020 — Graphs and Network Algorithms for Everyone [Register] 🆕

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