Interquartile Range https://t.co/eZ2bbpDzwV pic.twitter.com/YyGzlZJlPs
— Chris Albon (@chrisalbon) April 16, 2019
Interquartile Range https://t.co/eZ2bbpDzwV pic.twitter.com/YyGzlZJlPs
— Chris Albon (@chrisalbon) April 16, 2019
📝 Great write-up on types and uses of documentation.
— Mara Averick (@dataandme) April 16, 2019
❓ "What Docs When" by @gvwilsonhttps://t.co/Ju1jdT6TGU
/* 🖍 scribbles mine */ pic.twitter.com/sXNYC3e2y3
A great GitHub repository with tutorials on getting started with PyTorch and TorchText for sentiment analysis in Jupyter Notebooks. What a great resource! https://t.co/XSoUIwwYJ8
— Sebastian Raschka (@rasbt) April 16, 2019
I just taught a 2 days course on #dataviz with #rstat. I share the course material in case it can help somebody:https://t.co/eEVe9yGd2Y
— Yan Holtz (@R_Graph_Gallery) April 15, 2019
Included: DataViz intro & caveats, ggplot2, R Markdown, and Github intro pic.twitter.com/WyGKZZhoCP
🛠 Step-by-step to automate that pipeline w/ GitLab…
— Mara Averick (@dataandme) April 14, 2019
"How to easily automate R analysis, modeling and development work using CI/CD, w/ working examples" 👷♂️ @jozefhajnalahttps://t.co/3XVAGa9W0t #rstats pic.twitter.com/PDX5Yd20aY
new blog post! how to implement the super learner 📖 using #rstats #tidymodels infrastructure 📡
— alex hayes (@alexpghayes) April 14, 2019
the super learner is my favorite ensembling strategy, and wow is it cool to see how pleasant the code has become! https://t.co/fEaWrJuc3u pic.twitter.com/iTt2jpDmuq
All statistical conclusions require assumptions. https://t.co/CGihD7tzHd
— Andrew Gelman (@StatModeling) April 14, 2019
⏰ time-series package *and* cheat sheet…
— Mara Averick (@dataandme) April 14, 2019
"tsbox 0.1: class-agnostic time series" ⏱ @cynkrahttps://t.co/OY4Lo6slFE #rstats pic.twitter.com/YdT4z5vwVD
Our classification of #DataScience tasks is now in print.
— Miguel Hernán (@_MiguelHernan) April 13, 2019
We can only do 3 things with data:
1. description
2. prediction
3. counterfactual prediction (including #causalinference)
Each task requires different data, methods, and subject-matter knowledge.https://t.co/8Rrp9P5Dxx
Several reviews of Deborah Mayo’s new book, Statistical Inference as Severe Testing: How to Get Beyond the S https://t.co/pwamtK9fvs
— Andrew Gelman (@StatModeling) April 12, 2019
"Pretty nice" is quite the understatement. This is a wonderful in-depth discussion of the weird interactions between batchnorm, weight decay, and learning rate, including a fascinating experiment that shows that you can entirely replace weight decay with learning rate changes, https://t.co/DuCaJRRp91
— Jeremy Howard (@jeremyphoward) April 11, 2019
We just added a new tutorial that shows how to write Transformer (‘attention’ is all you need) in #TensorFlow 2.0 from scratch.
— TensorFlow (@TensorFlow) April 11, 2019
Check it out here → https://t.co/6dmNGC9NkE pic.twitter.com/GY1LwdxySK