Thanks to @revodavid for reminding me about this BBC Graphics with #rstats Cookbook. Great resource. https://t.co/Fs0Fy8qtdH
— JD Long (@CMastication) December 27, 2019
Thanks to @revodavid for reminding me about this BBC Graphics with #rstats Cookbook. Great resource. https://t.co/Fs0Fy8qtdH
— JD Long (@CMastication) December 27, 2019
New https://t.co/pjoVjWgRIy blog post by @kenny_ning about how we debug changes in conversion rates using machine learning https://t.co/F4DB7l4PrT
— Erik Bernhardsson (@fulhack) December 27, 2019
Bayes error pic.twitter.com/FK4ZZaQtns
— Chris Albon (@chrisalbon) December 27, 2019
Short Attention Span Theatre: Reproducing Axios’ “1 Big Thing” Google Trends 2019 News In Review with {ggplot2} https://t.co/l0x2mdb3mB #rstats pic.twitter.com/CHPd0NJwJq
— boB Rudis (@hrbrmstr) December 27, 2019
How to Read a Paper, by Srinivasan Keshav.https://t.co/nLdlsx7fPB pic.twitter.com/hyZnqBLAjd
— hardmaru (@hardmaru) December 25, 2019
Bagging vs. dropout. pic.twitter.com/5mtwgKmlTb
— Chris Albon (@chrisalbon) December 25, 2019
📆 Day 24 #rstats resource advent…
— Mara Averick (@dataandme) December 23, 2019
🚧 A WIP that contains multitudes of wisdom:
🤔 "What They Forgot to Teach You About R" by @JennyBryan & @jimhester_https://t.co/sNrrssTkZg ← 🐐 URL pic.twitter.com/gixB4UCmeD
Besides L2 loss, what are the other loss functions that are also consistent for the mean? Great post on loss functions for Lyft’s ETA estimation problem: https://t.co/aS6YEWNpUu
— Sean J. Taylor (@seanjtaylor) December 23, 2019
I've started writing a tutorial showing how to incrementally add fastai functionality to existing PyTorch code, which includes the above example. There's plenty more to come, but here's the start of it: https://t.co/m6WKyfXoUX
— Jeremy Howard (@jeremyphoward) December 20, 2019
“Nature does not shuffle the data, so we shouldn't either”—Leon Bottou (ICML 2019 Keynote)
— Sebastian Ruder (@seb_ruder) December 19, 2019
via @dabelcs' fantastic @NeurIPSConf notes: https://t.co/7LVZzdATta
An amazing overview of advances in NLP and ML in 2019 by @xamat
— Leonid Boytsov (@srchvrs) December 19, 2019
Among other things:
1. LM and masked LMs
2. semi/self-supervision in general
3. Injecting knowledge/structure into deep modelshttps://t.co/AVfnvgiQRN
For all of you who have been asking for a tutorial on how to develop complete python projects with @ProjectJupyter and nbdev - we now have step-by-step video and webpage tutorials for you!
— Jeremy Howard (@jeremyphoward) December 18, 2019
(The video is immediately after the table of contents.)https://t.co/XH1gsz8eDO