How to Make an Awesome Python Package in 2021 https://t.co/v57Rsievx8
— PyCoder’s Weekly (@pycoders) April 17, 2021
How to Make an Awesome Python Package in 2021 https://t.co/v57Rsievx8
— PyCoder’s Weekly (@pycoders) April 17, 2021
Thanks for posting @CaroPetitjean!
— Oriol Vinyals (@OriolVinyalsML) April 15, 2021
Here are the slides: https://t.co/RwWqFnZset https://t.co/jb8wNTSWTL pic.twitter.com/hQxZDKh5RG
The @scikit_learn team at @sklearn_inria has created a MOOC for getting started with scikit-learn! Go check it out! https://t.co/BUbDArJJDU
— Andreas Mueller (@amuellerml) April 14, 2021
You can leave feedback and suggestions (or make PRs) on the repo here: https://t.co/RCMWxqGWb2
I just finished giving my keynote at Nvidia GTC'21.
— Yann LeCun (@ylecun) April 13, 2021
Slides are here: https://t.co/9GogK14k9D
The Bayesian modeling equivalent is this @StatModeling @avehtari @dan_p_simpson et al. Bayesian workflow paper: https://t.co/aJSMVxCvA1
— Sean J. Taylor (@seanjtaylor) April 13, 2021
Having strict process to ensure good results is so important, no matter how much experience you have. Very easy to go off the rails otherwise.
🎥Modern Artificial Intelligence 1980s - 2021 by @SchmidhuberAI!
— Radek Osmulski (@radekosmulski) April 13, 2021
This talk delivers! 🙂
✅ starts with the Big Bang (literally)
✅ history of everything explained in the first 10 minutes
✅ only accelerates from there 🙂
👉 https://t.co/Ya7NwvRaTq
Here is what I learned... pic.twitter.com/dMCfVH2PvD
10 Things You Need to Know About BERT and the Transformer Architecture That Are Reshaping the AI Landscape
— Sebastian Ruder (@seb_ruder) April 9, 2021
This super comprehensive post covers most things that are important in current NLP including BERT, transfer and avocado chairs 🥑
by @cathalhoran https://t.co/Hn458pkrk8 pic.twitter.com/srs4bIFrFw
New code walkthrough on https://t.co/m6mT8SrKDD: using a siamese network to learn to estimate how similar two images look like -- trained on the "Totally Look Like" dataset. Super readable and nicely explained. Created by @hazemessamm & @svpino
— François Chollet (@fchollet) March 26, 2021
Check it: https://t.co/9wruW7oyDL pic.twitter.com/X7RLmsxiEC
This talk was 🔥🔥🔥!
— Radek Osmulski (@radekosmulski) March 24, 2021
Here are my notes: https://t.co/2znd4zhgjb
So many takeaways that go against beliefs held by many:
✅ engineering is very important to good data science
✅ early teaming > late teaming
✅ you can do very well with few resources
✅ learn, learn, learn https://t.co/kvE2AOQAXM
“You Can’t Sit With Us”: Exclusionary Pedagogy in AI Ethics Education by @rajiinio @morganklauss @amironesei https://t.co/8PI11rk6wI pic.twitter.com/T3tsUAh2AM
— Rachel Thomas (@math_rachel) March 24, 2021
"The likelihood is dead, long live the likelihood"
— Kyle Cranmer (@KyleCranmer) March 22, 2021
An article on simulation-based (aka likelihood-free) inference in the CERN newsletter by Johann Brehmer and me. https://t.co/BS8RaS8Q1E pic.twitter.com/Ga0ryfRkbB
Happy to share our tutorial's video on "Causal Fairness Analysis" in the @FAccTConference this year (joint work w/ Drago Plecko & @junzhez): https://t.co/Nn67gjczLw, and the slides: https://t.co/nyd0bmNJse. Special thanks to the organizers, @tiberiocaetano & @zacharylipton .)
— Elias Bareinboim (@eliasbareinboim) March 17, 2021