Watch "Getting it right: Writing reliable and maintainable R code" with @ajmcoqui from rstudio::conf(2019)
— RStudio (@rstudio) October 19, 2019
🎦 https://t.co/6Uc3GHDG4T
Learn more about and register for rstudio::conf(2020) in San Francisco at https://t.co/rYJqkzCywm #rstudioconf #rstats #DataScience
Watch "gganimate live cookbook" with @thomasp85 from rstudio::conf(2019)
— RStudio (@rstudio) October 17, 2019
🎦 https://t.co/rEhn6YDpEH
Learn more about and register for rstudio::conf(2020) in San Francisco at https://t.co/rYJqkzCywm #rstudioconf #rstats #DataScience pic.twitter.com/FHgC2NFyBL
In this #tidytuesday screencast, I analyze of fuel efficiency in cars (like mtcars, but with 42K obs of 84 variables!)
— David Robinson (@drob) October 15, 2019
Did you know that most cars have higher fuel efficiency on highways, but most hybrids have higher efficiency in cities? 🤯🚘🚗https://t.co/PZdt6h3PAe #rstats pic.twitter.com/sfQ9yAZoLl
Love R? Watch "Interfacing R and Python" from PyData Berlin 2018 #rstats #pythonhttps://t.co/8XtsOIThEM
— PyData (@PyData) October 14, 2019
The video of my talk at WeCNLP *2018* last fall is (finally) online.
— Thomas Wolf (@Thom_Wolf) October 8, 2019
What a different time!
I had 500 (lovely) followers on twitter and was already talking about SOTA with Transformers & Transfer Learning but the Bert craziness had not yet happened...https://t.co/aQtdYcUaGo
"An introduction to Model Interpretability wiht LIME and SHAP" from PyData New York 2018https://t.co/52jew6eOvH
— PyData (@PyData) October 5, 2019
Don't forget to subscribe to #PyData on Youtube!
🗣 Attention #TensorFlow community!
— TensorFlow (@TensorFlow) September 30, 2019
We are excited to announce TensorFlow 2.0. Inspired by all of your feedback, this powerful and easy-to-use framework includes tight Keras integration, expanded TF datasets, and more.
Watch @lmoroney present here → https://t.co/bzI1vDZDnV pic.twitter.com/uoPvOXJnzP
This edited version is an improvement, but
— Judea Pearl (@yudapearl) September 28, 2019
my favorite video is still the one I gave in Paris:https://t.co/khr90KUzXa
(Microsoft, 2012) perhaps because the ideas were still new to me, fresh from the brewery, or because I was 7 years younger. #Bookofwhy https://t.co/purDF1M6Cu
I enjoyed this talk by @msalvaris using Dask, @PyTorch, and GPUs on @Azure to accelerate computer vision workloads:https://t.co/uXeWxglooK pic.twitter.com/Xl6MCyCzzg
— Dask (@dask_dev) September 25, 2019
Join @rctatman for coffee with David Ha (@hardmaru), Google researcher and co-author of the NeurlPS paper, "Weight Agnostic Neural Networks". Listen as they talk about reinforcement learning, and what ML researchers can learn from horses. 🤔🐎 [WATCH] https://t.co/IHAmarBCW6
— Kaggle (@kaggle) September 24, 2019
Want to see @dask_dev scale up from #NumPy on CPU to @ChainerOfficial #CuPy + @numba_jit on @NVIDIA #GPU in seconds and get orders of magnitude more performance... https://t.co/GXWOZDaoT2 Yeah, we're just as impressed as you are.
— RAPIDS AI (@rapidsai) September 19, 2019
Stuart Russell: "Social media algorithms learned that the easiest way to predict people's clicks was not to become better at personalization, but to make people more predictable by radicalizing them" 🤯 https://t.co/pxZUZMG5oA
— Xavier Amatriain (@xamat) September 19, 2019
