Source: Deep Learning on Medium
D4S Sunday Briefing #30
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Dec 22, 2019
Welcome to the Dec 22nd issue of the Sunday Briefing. This Holiday week we take a dive into Information Theory, the Bias Variance Tradeoff, some tips for analyzing network data and a Bayesian take on Rule Mining.
Finally, in the video of the week Jeffrey Yau guides us through Applied Time Series Econometrics in Python and R in this tutorial from PyData 2016 in San Francisco.
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Our sincerest wishes for a wonderful holiday week surrounded by family and loved ones.
The D4S team
Tutorials and blog posts that came across our desk this week.
- The Machines Are Learning, and So Are the Students [nytimes.com]
- Google’s AI Chief Wants to Do More With Less (Data) [wired.com]
- A comprehensive guide to OCR with Tesseract, OpenCV and Python [nanonets.com]
- Probability and Information Theory with Tensorflow [adhiraiyan.org]
- This AI researcher is trying to ward off a reproducibility crisis [nature.com]
Fresh off the press:
Some of the most interesting academic papers published recently.
- Nine quick tips for analyzing network data (V. Miele, C. Matias, S. Robin, S. Dray)
- Fact-checking strategies to limit urban legends spreading in a segregated society (M. Tambuscio, G. Ruffo)
- On the Bias-Variance Tradeoff: Textbooks Need an Update (B. Neal)
- Extending Machine Language Models toward Human-Level Language Understanding (J. L. McClelland, F. Hill, M. Rudolph, J. Baldridge, H. Schütze)
- A Bayesian Approach to Rule Mining (L. I. L. González, A. Derungs, O. Amft)
- Ten AI Stepping Stones for Cybersecurity (R. Morla)
Video of the week:
Interesting discussions, ideas or tutorials that came across our desk.
Applied Time Series Econometrics in Python and R
Opportunities to learn from us
- Jan 17, 2019 — Time Series for Everyone [Register]
- Jan 27, 2019 — Applied Probability Theory for Everyone [Register] 🆕
- Mar 15–16, 2019 — Time series modeling: ML and deep learning approaches — Strata/AI [Register]
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