D4S Sunday Briefing #30

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.

Issue #30

Dec 22, 2019

Dear friends,

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.

Semper discentes,

The D4S team

Top Links:

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

  1. The Machines Are Learning, and So Are the Students [nytimes.com]
  2. Google’s AI Chief Wants to Do More With Less (Data) [wired.com]
  3. A comprehensive guide to OCR with Tesseract, OpenCV and Python [nanonets.com]
  4. Probability and Information Theory with Tensorflow [adhiraiyan.org]
  5. 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.

Video of the week:

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

Applied Time Series Econometrics in Python and R

Upcoming Events:

Opportunities to learn from us

  1. Jan 17, 2019 — Time Series for Everyone [Register]
  2. Jan 27, 2019 — Applied Probability Theory for Everyone [Register] 🆕
  3. Mar 15–16, 2019 — Time series modeling: ML and deep learning approachesStrata/AI [Register]

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