Also, GBMs can often do better on these kinds of problems. The winner of the competition discussed in the article found GBMs better than RFs, and factorization machines better still. pic.twitter.com/KjsanRXcyb
— Jeremy Howard (@jeremyphoward) January 31, 2020
John Schulman is one of the researchers I admire most in the field. Awesome guide for how to do a PhD / break into research! I need to implement more papers from scratch... https://t.co/5gHlTm7DtL
— Eric Jang 🇺🇸🇹🇼 (@ericjang11) January 31, 2020
A comparison of message queues https://t.co/27OoFrhyG2 (by @archerabi from our tech blog)
— Erik Bernhardsson (@fulhack) January 31, 2020
The crux of learning is when the learner feels smart. Not the teacher.
— JD Long (@CMastication) January 31, 2020
And I have @kjhealy Data Viz book on my desk. It makes me be able to do neat things... https://t.co/uMRtOsPpIB
OpenAI Switches to PyTorch https://t.co/A1mZX0lpgZ
— /MachineLearning (@slashML) January 30, 2020
I was gifted "Hackers and Painters" by Paul Graham and it seems like a case study for Dunning-Kruger and I'm sad. He describes how naive Bayes works for spam filtering though (in 2004, which is much after it was introduced, but early for a general tech person to talk about ML).
— Andreas Mueller (@amuellerml) January 30, 2020
A great interview with Galit Shmueli @gshmueli on fairness and ethics in data science. https://t.co/yAIXVAPsy6
— Rob J Hyndman (@robjhyndman) January 29, 2020
At #rstudioconf @jtleek talks about addressing income inequality through access to education:
— Julia Silge (@juliasilge) January 29, 2020
Talent is evenly distributed but opportunity isn't.
Most people who took Jeff's MOOCs were already educated, white, male, etc.
Can this be changed to increase access? 🤔 pic.twitter.com/TX9FnmU1tT
Data horror stories from @fly_upside_down - when asked to share data back in the early 90s, they **printed out and shipped the data** for the other group to re-enter 😱 #rstudioconf
— Emily Robinson (@robinson_es) January 29, 2020
vscode-python users: please upvote the smart select support for Python code: https://t.co/SQ8ssMy4dw . This would be helpful to refactor code without having to use the mouse to select structured code sections.
— Olivier Grisel (@ogrisel) January 29, 2020
Here is how it looks for Javascript code: https://t.co/UqI1ZeLSSK
Great tech story! How IT got to the bottom of a complaint starting with "We can't send mail more than 500 miles." #programming
— Randy Olson (@randal_olson) January 28, 2020
I really enjoyed the part about the statistics department's approach to the problem.
Read the full story here: https://t.co/O0m42EGrmV pic.twitter.com/0BFBmNdIew
Applying machine learning to the real world is commonly an optimization problem on top of the ML predictions. My former colleague @dan_s_becker built and awesome demo of this at https://t.co/xtpruhCSR6 with "The Mango Challenge", and this is the core thesis for his new company
— Ben Hamner (@benhamner) January 28, 2020
