"Six Reasons You Need a More Diverse Data Science Team" by @amcasari #DEVcommunity https://t.co/jfKeK2v6Vh
— commandasaurus 🦕 (@amcasari) June 15, 2019
"Six Reasons You Need a More Diverse Data Science Team" by @amcasari #DEVcommunity https://t.co/jfKeK2v6Vh
— commandasaurus 🦕 (@amcasari) June 15, 2019
Please share this with the people in your life who are convinced precision medicine is an unassailable good:https://t.co/pSPD0IjJas
— Cathy O'Neil (@mathbabedotorg) June 14, 2019
ICYMI, 🔥 From the basics to predictive analytics, this is fire!
— Mara Averick (@dataandme) June 14, 2019
📕 "UC Business Analytics R Programming Guide" by @bradleyboehmke https://t.co/3n3GcJl4q6 via @UC_Rstats #rstats pic.twitter.com/KgM0bdphqw
My AutoML keynote will be streaming live from this link, starting in 5 mins #icml2019https://t.co/4geXi8EIwo
— Rachel Thomas (@math_rachel) June 14, 2019
Erin LeDell presenting her poster on the open source AutoML benchmark
— Rachel Thomas (@math_rachel) June 14, 2019
Details here: https://t.co/OjaBUyiPC9 #icml2019 @ledell @wimlds pic.twitter.com/xTPr1bQjhf
"Competing helps develop an intuition for what might work or not." | Best practices from @el_PA_B after competing in the Freesound Audio Tagging competition. Read more 👉 https://t.co/YnJLshK9RT via @TDataScience pic.twitter.com/s6eDttMnj1
— Kaggle (@kaggle) June 14, 2019
Paper: https://t.co/V7U4FpYMb9
— Victor Zhong (@hllo_wrld) June 14, 2019
Dockerized code: https://t.co/gWocV5qmPn
10/10#nlproc#ACL2019NLP#DeepLearning
Our #acl2019nlp paper Entailment-driven Extracting and Editing for Conversational Machine Reading (E3) is out! E3 studies conversational machine reading (CMR), a task oriented dialogue problem where reasoning rules aren't fixed but implied by procedural text. Paper/code👇1/10 pic.twitter.com/v6Ro6wOaik
— Victor Zhong (@hllo_wrld) June 14, 2019
We really ought to rethink recommender systems. A decade ago with @mikeklaas at @Flipboard, then at our startup Zite, we discussed empowering users with information pull interfaces based on tags, instead of having naive AI algorithms deciding for users. Time for research. https://t.co/fFOoNed4j7
— Nando de Freitas (@NandoDF) June 14, 2019
Use `pip search <your_keyword>` to search PyPI for all packages with <your_keyword> in the short description.
— Daily Python Tip (@python_tip) June 14, 2019
Good thread. One of the most important skills AI teams & PMs need to develop in industry is the ability to tell the difference between easy, hard, and impossible machine learning problems. https://t.co/gJUq4lbg2v
— Peter Skomoroch (@peteskomoroch) June 14, 2019
ICYMI, 🧐 @cpsievert's plotly book covers all sorts of interesting stuff I never knew existed…
— Mara Averick (@dataandme) June 14, 2019
📒 "Interactive web-based data visualization w/ R, plotly, and shiny" https://t.co/fF2ttLJUf3 #rstats #dataviz pic.twitter.com/NBLd1xcd3x