What causes the spread of misinformation? Scientists are learning more about the drivers—which include authors, publishers, articles, rumors, and the public. ($) https://t.co/69vTKbU2fZ pic.twitter.com/XkOXuHcfyi
— Science Magazine (@sciencemagazine) January 29, 2019
What does your Kaggle profile communicate to recruiters?
— Kaggle (@kaggle) January 28, 2019
Read: "How to Find a Strong Developer Using StackOverflow and Kaggle" 👉https://t.co/NREGnIcdeU #datascience #jobs pic.twitter.com/EeFHxlssul
“Why are Machine Learning Projects so Hard to Manage?” by @l2k https://t.co/VMubnQnnpV
— Peter Skomoroch (@peteskomoroch) January 28, 2019
Missed out on #rstudioconf? Check out @brookLYNevery1's excellent summary on the five lessons learned, with links to the relevant talks (the recordings of which are mostly online!): https://t.co/bZkUN9GoZM pic.twitter.com/UaXz9wFM4d
— Emily Robinson (@robinson_es) January 28, 2019
Want to be more creative? Try a spot of slow-motion multitasking. My new @TEDTalks talk: https://t.co/kC5MUwkGpb
— Tim Harford (@TimHarford) January 28, 2019
I was international student once, and I felt left out when a bunch students from another country kept switched the conversation to their language (German in this case). Instead of being dogmatic, I think all sides can err on the side of inclusivity.
— Delip Rao (@deliprao) January 27, 2019
"Over the last three years, he has been involved in six different court cases across three continents. His crime? Making public information accessible to the public." Yep, that's @carlmalamud, the Gandhi of open government data https://t.co/BHWulN7eWZ
— Tim O'Reilly (@timoreilly) January 27, 2019
Here's a link to the commit, for those who are interested.https://t.co/UYwSj2HX5i
— Jeremy Howard (@jeremyphoward) January 27, 2019
“The way how Deepmind beat the human pros was in direct contradiction to what their mission statement was and what they repeatedly claimed to be the “right way” to do it.” 🔥🔥🔥 https://t.co/xqKn8w01A8
— Denny Britz (@dennybritz) January 27, 2019
Interesting post on AlphaStar's APM and superhuman actions. pic.twitter.com/KrzSZf73en
The "psychological equations" in machine learning are: proofs of error bounds.
— Jeremy Howard (@jeremyphoward) January 26, 2019
Write one, and you're almost guaranteed acceptance to the most exclusive conferences. Yet I've never seem them used for anything except for other error bound proofs. https://t.co/4kvn4fu1zX
"Learning well means allowing time to pass between focused study sessions, so the neural patterns have time to solidify.
— Rachel Thomas (@math_rachel) January 26, 2019
"It's like allowing time for the mortar to dry when building a brick wall. Trying to learn in just a few cram sessions results in a jumbled pile of bricks." https://t.co/1lMhe4wnM3
One key lesson that I learned in open source is the Apache(@TheASF) way of development. By keeping all discussions and decisions in public archive, we enable everyone's participation. While this approach brings a bit overhead, it is the best way to grow a healthy community.
— Tianqi Chen (@tqchenml) January 26, 2019
