Tag: learning

It is embarrassing how many times I have to look at this: https://t.co/LLwYSTKm8X pic.twitter.com/IVHieZ0Dn2

— Chris Albon (@chrisalbon) September 9, 2018

Data Anonymization by Bartlomiej Uscilowski - https://t.co/xgsiQriM0Q. This talk discusses two modules: faker and mimesis, for anonymizing customer data.

— Python Software (@ThePSF) September 9, 2018

💖 fresh modeling material from @topepos & Kjell Johnson:
📖 "Feature Engineering and Selection: A Practical Approach for Predictive Models" https://t.co/q6TUcTsfkI
* data and #rstats code: https://t.co/zrLDd8hMuf pic.twitter.com/7Ag298iZiW

— Mara Averick (@dataandme) September 9, 2018

New chapters of our Feature Engineering and Selection book were added (interaction detection and missing data). https://t.co/bNeNSVDj3G #rstats pic.twitter.com/yRv99AXhTz

— Max Kuhn (@topepos) September 9, 2018

A (Long) Peek into Reinforcement Learning
By @lilianwenghttps://t.co/NpKaSpFQ88 pic.twitter.com/aaT7PPYcFU

— ML Review (@ml_review) September 9, 2018

ICYMI, 🎉 video from #rstatsnyc is up!
😹 Feat. so much amazing!
📺 "New York R Conference: 2018" https://t.co/DgRQjcpIrz #rstats pic.twitter.com/YXjjQtXZur

— Mara Averick (@dataandme) September 8, 2018

Those interested in forecasting you can look at the new blog by Slawek Smyl of Uber whose Hybrid method produced the most accurate forecasts in the M4 Competition. It provides an excellent introduction to business forecasting.
https://t.co/Eo6feY6jp3@SlawekSmyl

— Spyros Makridakis (@spyrosmakrid) September 8, 2018

It's back-to-school time everyone!

New post on "The Blessings of Multiple Causes" by @yixinwang_ and @blei_labhttps://t.co/lf54Xw6L45

— Ferenc Huszár🇪🇺 (@fhuszar) September 7, 2018

🎓 oh the things you can do with Rmd…
"Reproducible academic writing w/ RMarkdown" 🗣 @sauer_sebastian https://t.co/g6H3w3yVOr #rstats #rmarkdown pic.twitter.com/uQoXa9dtyd

— Mara Averick (@dataandme) September 6, 2018

Attention is one of the most important breakthroughs in the history of Deep Learning - and this @distillpub is definitively the best explanation of it I've seen.

For #100DaysOfMLCode challengers - try implementing an attention mechanism from scratch.https://t.co/uOAMulnuoq

— Trask (@iamtrask) September 6, 2018

Look at this goooooorgeous and super informative explainer by @kosamari! 😍 https://t.co/9UyW4DXwjL

— Monica Dinculescu (@notwaldorf) September 6, 2018

If you want to understand the inner workings of machine learning and deep learning models, @_brohrer_ has shared his *amazing* posts and videos here: https://t.co/smYb26aVW0

— David Smith (@revodavid) September 5, 2018