ICYMI, 🎞 step-by-step monte carlo w/ magick!
— Mara Averick (@dataandme) June 19, 2018
"Animating a Monte Carlo Simulation" by Thomas Rohhttps://t.co/YOCtIYLZBM #rstats #dataviz #infovis pic.twitter.com/XZJsXI2YNS
ICYMI, 🎞 step-by-step monte carlo w/ magick!
— Mara Averick (@dataandme) June 19, 2018
"Animating a Monte Carlo Simulation" by Thomas Rohhttps://t.co/YOCtIYLZBM #rstats #dataviz #infovis pic.twitter.com/XZJsXI2YNS
I love do 🖤 a good workflow…
— Mara Averick (@dataandme) June 19, 2018
"File organization best practices" by @abtran https://t.co/PE7lLypdkX #rstats pic.twitter.com/ZvRX244aAr
Today at #CVPR2018 we introduce @NVIDIA DALI & nvJPEG, new #deeplearning libraries for data augmentation and image decoding. https://t.co/nndrnQBG1a pic.twitter.com/OAvs7ixpMl
— NVIDIA AI Developer (@NVIDIAAIDev) June 19, 2018
How might visualizations help people reason more cautiously in the face of uncertainty? Value-Suppressing Uncertainty Palettes intentionally make uncertain values harder to distinguish! https://t.co/TciyeqjSHs
— Interactive Data Lab (@uwdata) June 19, 2018
Apex is a PyTorch extension from @nvidia that makes it easy to use mixed-precision training and use Volta Tensor Cores to full potential. Read more at: https://t.co/9xuOLAyGJT
— PyTorch (@PyTorch) June 19, 2018
What is the role of qualitative methods in addressing issues of replicability, reproducibility, and rigor? https://t.co/aJ33RXF8wx
— Andrew Gelman (@StatModeling) June 19, 2018
The long-awaited "Visual Introduction to #MachineLearning Part II" is now out, focusing on model tuning and the bias-variance tradeoff. #datavizhttps://t.co/5uhkaHVaUl pic.twitter.com/YocmhXd6ZB
— Randy Olson (@randal_olson) June 18, 2018
ICYMI, 👍 overview:
— Mara Averick (@dataandme) June 18, 2018
"Enterprise Dashboards with R Markdown" by @nwstephens
https://t.co/YwE2q5K8dE #rstats #rmarkdown #dataviz pic.twitter.com/PvLn7vA22V
10 common security gotchas in #Python and how to avoid them. #programminghttps://t.co/mDxC583eu5 pic.twitter.com/U084t3D3TB
— Randy Olson (@randal_olson) June 18, 2018
JupyterHub 0.9 (multi-user notebook server) released: https://t.co/ufFBUzEQ3n
— Min RK (@minrk) June 18, 2018
I've been thinking about why this is counterintuitive, even (especially?) to people experienced in probability. I think it's because effects like linearity of expected value spoil us in being able to treat events as independent even if they aren't https://t.co/mJd8h4vVGy
— David Robinson (@drob) June 18, 2018
bounceR 0.1.2: Automated Feature Selection https://t.co/BgNPjaUwFB #rstats #DataScience
— R-bloggers (@Rbloggers) June 18, 2018