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.
Apr 19, 2020
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.
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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
Tutorials and blog posts that came across our desk this week.
- Forecasting s-curves is hard [constancecrozier.com]
- Event-Reduce — An algorithm to optimize database queries that run multiple times [github.com/pubkey]
- The Hungarian Statistician Behind Three Volumes of Visualization Masterpieces [medium.com/nightingale]
- Interpretability [fastforwardlabs.com]
- How to know if artificial intelligence is about to destroy civilization [technologyreview.com]
- Building a Social Network from the News using Graph Theory [towardsdatascience.com]
- Monitoring Machine Learning Models in Production [christophergs.com]
Fresh off the press:
Some of the most interesting academic papers published recently.
- Social-media companies must flatten the curve of misinformation (J. Donovan)
- The Diversity–Innovation Paradox in Science (B. Hofstra, V. V. Kulkarni, S. M.-N. Galvez, B. He, D. Jurafsky, D. A. McFarland)
- Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (M. Brundage, S. Avin, J. Wang, H. Belfield, G. Krueger, et al)
- Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies (M. Nagendran, Y. Chen, C. A. Lovejoy, A. C. Gordon, M. Komorowski, H. Harvey, E. J. Topol, J. P. A. Ioannidis, G. S. Collins)
- Shortcut Learning in Deep Neural Networks (R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, F. A. Wichmann)
- Datasets for Data-Driven Reinforcement Learning (J. Fu, A. Kumar, O. Nachum, G. Tucker, S. Levine)
- Oversampling for Imbalanced Time Series Data (T. Zhu, Y. Lin, Y. Liu)
- An In-depth Walkthrough on Evolution of Neural Machine Translation (R. Jagtap, S. N. Dhage)
Video of the week:
Interesting discussions, ideas or tutorials that came across our desk.
A Taste of GPU Compute
Opportunities to learn from us
- Apr 29, 2020 — Applied Probability Theory for Everyone [Register]
- May 7, 2020 — Natural Language Processing (NLP) for Everyone [Register] 🆕
- May 18, 2020 — Graphs and Network Algorithms for Everyone [Register] 🆕
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