vroom #rstats 📦, because life is too short to read slow! 🏎️💨
— Jim Hester (@jimhester_) July 12, 2019
slides 📽️ from my #useR2019 talk available at https://t.co/3h6b2m7lQO
vroom #rstats 📦, because life is too short to read slow! 🏎️💨
— Jim Hester (@jimhester_) July 12, 2019
slides 📽️ from my #useR2019 talk available at https://t.co/3h6b2m7lQO
💞 this post from @willkurt about the relationship between Bayes' Theorem and logistic regression! I have never seen this before, and it made me 🤯 in the best way. Will's narrative and walkthrough is top notch.https://t.co/tsNAS5qNxs pic.twitter.com/wsXho5y5yT
— Julia Silge (@juliasilge) July 12, 2019
How can weak supervision help when you have limited labeled data? In our latest blogpost, we explore how to programmatically create labels with weak supervision. https://t.co/yUXhUjZBZe
— Fast Forward Labs (@FastForwardLabs) July 11, 2019
Slides & some other materials from yesterday's #UserR2019 #rstats tutorials now on GitHub: https://t.co/3AxBZDsFjU pic.twitter.com/3ti9YW8AKQ
— Sharon Machlis (@sharon000) July 11, 2019
Fixed slide deck:https://t.co/BTbe4AhNSU
— Gael Varoquaux (@GaelVaroquaux) July 11, 2019
I'm removing the previous one, that had errors, sorry for the broken links
Take a live look at how I'm building my tutorial "A Real-Life Massive Machine Learning Pipeline with scikit-learn". Hope to release it soon. Big thanks to @mybinderteam #machinelearning #MasterDataAnalysisWithPython https://t.co/GSERYJyUGf
— Ted Petrou (@TedPetrou) July 10, 2019
Next at #useR2019 @dzj_evalparse shares about disk.frame 💾, a framework for data manipulation in #rstats for medium datasets (larger than RAM, smaller than "big data").https://t.co/akpnZlF7m6 pic.twitter.com/Go3eb6sU88
— Julia Silge (@juliasilge) July 10, 2019
Missed my rstudio::conf talk? If you're at #useR2019, you've got another chance: come hear about data science puzzles 🧩 today at 11:48am in Cassiopée
— Irene Steves (@i_steves) July 10, 2019
Materials: https://t.co/JKOB1AjHUX pic.twitter.com/7L2nayZR08
I started seeing type hints in Python code I was working on about a year ago now.
— Vicki Boykis (@vboykis) July 9, 2019
My first thought: Nice!!
My second thought: Wait, what are type hints?
So I recently did a deep dive on what type hints bring to Python and whether we need to use them. https://t.co/cR1k1o0uVb
A Survey on Image Data Augmentation for Deep Learning [48pp]
— ML Review (@ml_review) July 9, 2019
By @CShorten30
Geometric transform, color space augment, kernel flters, mixing images, random erasing, feature space augment, adversarial training,
GANs, neural style transfer & meta-learning https://t.co/6DKAcbPASK pic.twitter.com/LWeT4okVSY
I updated the slide deck from my "Introduction to Go" at @CERN earlier this year
— francesc (@francesc) July 8, 2019
It now includes links to the recordings for each day!
⚛︎⌨️😎🎥
day 1: https://t.co/nCJunW1iIT
day 2: https://t.co/QA67i4WfUa
day 3: https://t.co/MmT6WaA1ie#golanghttps://t.co/IBg11aW8ny
A short blogpost motivating and highlighting simple HTML outputs in Jupyterhttps://t.co/33J0cYE7OH pic.twitter.com/PjwXhhRPSH
— Matthew Rocklin (@mrocklin) July 8, 2019