Untitled2.ipynb pic.twitter.com/iVAIDCKxIB
— Chris Albon (@chrisalbon) May 25, 2019
Untitled2.ipynb pic.twitter.com/iVAIDCKxIB
— Chris Albon (@chrisalbon) May 25, 2019
test pic.twitter.com/0r2b7236HX
— Chris Albon (@chrisalbon) May 25, 2019
In this week's #tidytuesday screencast, I analyze a dataset on Nobel Prize winners ⚛️☮️⚕️🏅
— David Robinson (@drob) May 24, 2019
Something I learned is that scientists used to win within 10 years of publishing the prize-winning work, and now typically wait 25 years 😯https://t.co/5hs3J5Dw3m #rstats pic.twitter.com/5XjV8PP4Fj
"Automated Machine Learning: Methods, Systems, Challenges" 📖
— ML Review (@ml_review) May 22, 2019
Free Book by @FrankRHutter @larskotthoff @joavanschorenhttps://t.co/O7FID0BmTEhttps://t.co/Qk8EKFJFxW pic.twitter.com/Uw8PFxOYcS
Interquartile Range https://t.co/eZ2bbpDzwV pic.twitter.com/6ggkmlR456
— Chris Albon (@chrisalbon) May 22, 2019
New on the @FastForwardLabs blog: @shioulin_sam and @NishaMuktewar dive into meta-learning and learning with limited labelled data: https://t.co/lOHx1r5k9S
— Hilary Mason (@hmason) May 22, 2019
CleverHans blog post with @nickfrosst: we explain how the Deep k-Nearest Neighbors (DkNN) and soft nearest-neighbor loss (SNNL) help recognize data that is not from the training distribution. The post includes an interactive figure (credit goes to Nick): https://t.co/aajpf8NOib pic.twitter.com/MKKc4WX8Rp
— Nicolas Papernot (@NicolasPapernot) May 21, 2019
Meanshift Clustering By Analogy https://t.co/eZ2bbpDzwV pic.twitter.com/pZ06idRqk2
— Chris Albon (@chrisalbon) May 13, 2019
An interactive article explaining why weight initialization is so important for training neural nets by @deeplearningai_, written in the https://t.co/pxiwhf2mAG format. https://t.co/IhwScvEao3 pic.twitter.com/lxJVDC3y6P
— hardmaru (@hardmaru) May 13, 2019
This recent blog post by Graves and Clancy at DeepMind gives a great summary/case for it: https://t.co/45k5ZGiKxy
— Miles Brundage (@Miles_Brundage) May 11, 2019
🔥 From the basics to predictive analytics, this is fire!
— Mara Averick (@dataandme) May 9, 2019
📕 "UC Business Analytics R Programming Guide" by @bradleyboehmke https://t.co/3n3GcJl4q6 via @UC_Rstats #rstats pic.twitter.com/NInYK6GKwD
Cool! Léon Bottou referenced our “Learning to Pivot with Adversarial networks” paper in his ICLR talk!@glouppe @Michael_A_Kagan
— Kyle Cranmer (@KyleCranmer) May 9, 2019
Our Paper: https://t.co/3Q1XRTWEbq
His talk: https://t.co/07Pd507obH pic.twitter.com/WeJ2JF40mS