🤝 for the bas[e]ics: “Base R Cheat Sheet” by @mhairihmcneill https://t.co/09s8ZrXhIo via @rstudio #rstats pic.twitter.com/jFN3In4XO0
— Mara Averick (@dataandme) July 1, 2018
🤝 for the bas[e]ics: “Base R Cheat Sheet” by @mhairihmcneill https://t.co/09s8ZrXhIo via @rstudio #rstats pic.twitter.com/jFN3In4XO0
— Mara Averick (@dataandme) July 1, 2018
New blog post: Measuring punctuation ⁉️ use in literature with #rstats https://t.co/FGrwOlvg6F pic.twitter.com/av9wRr9rcQ
— Julia Silge (@juliasilge) June 30, 2018
How to tell what your tree classifier is doing? A really nice kernel just showed up on @kagglehttps://t.co/ArqRdCDfDp
— Radek (@radekosmulski) June 30, 2018
⚽️ code-through!
— Mara Averick (@dataandme) June 29, 2018
"Visualize the #WorldCup with R! Pt 1: Recreating Goals w/ ggsoccer and ggplot2" ✍️ @R_by_Ryohttps://t.co/uLy0Qi1WvY #rstats #dataviz pic.twitter.com/TnpcZ8oFcn
We have a new post by @MannyMoss up on our blog about supercharging classification with multi-task learning: https://t.co/7WQ54sDCkM
— Fast Forward Labs (@FastForwardLabs) June 29, 2018
This week's #KernelAwards winner uses t-distributed Stochastic Neighbor Embedding (t-SNE) and a LGBMClassifier to determine what a smartphone user is doing: https://t.co/yL1RUxRr5m pic.twitter.com/EiH0ugS2TX
— Kaggle (@kaggle) June 29, 2018
Slides for my #MLITRW talk – Simple representations for learning: factorizations and similarities
— Gael Varoquaux (@GaelVaroquaux) June 29, 2018
On how to scale matrix factorization to huge data and how to use string similarities to learn on dirty categorical datahttps://t.co/iyAFpbE2w9
💡 brill idea (cluster ingredients), 🎬 and 📽!
— Mara Averick (@dataandme) June 29, 2018
"Cooking Up Statistics: The Science & the Art" 👩🏿🍳 @LetishaAudreyhttps://t.co/DChrwfPUIG #rstats via @RLadiesNYC #RLadies pic.twitter.com/0ixdnoDxM5
💖 great read:
— Mara Averick (@dataandme) June 29, 2018
“Explaining the Gap: Visualizing One’s Predictions ⇧ Recall & Comprehension of Data” by @uwdata https://t.co/r39V61uSJd #dataviz #infovis pic.twitter.com/vVq9mFnYkJ
Black-box recommendations are common in industry. This is a guide on the opposite: how to do real science with that latent space. @erinselene @iPancreas @stitchfix_algo https://t.co/5FNztXepjG pic.twitter.com/8gCqhJfVWZ
— christopher e moody (@chrisemoody) June 29, 2018
First figure from our paper: how the LSTM with a twist allows for the equivalent speed of a plain convnet by running efficiently in parallel on GPUs, like image processing convents.https://t.co/2PpYOkyBSn pic.twitter.com/gubGw92NT3
— Smerity (@Smerity) June 28, 2018
Multiverse ✕ tidyverse ⇨ 💥
— Mara Averick (@dataandme) June 28, 2018
"A tidy text analysis of Rick and Morty" 👨🚀 @tudosgarhttps://t.co/siXDEvYZOb #rstats #tidytext #textmining pic.twitter.com/NDC6qZIHLc