10 Predictions for Deep Learning in 2019 by @IntuitMachine https://t.co/GgWF76Xjiq pic.twitter.com/4FGJd8qp4l
— Kaggle (@kaggle) January 14, 2019
10 Predictions for Deep Learning in 2019 by @IntuitMachine https://t.co/GgWF76Xjiq pic.twitter.com/4FGJd8qp4l
— Kaggle (@kaggle) January 14, 2019
Much has been written about the harm of screens and digital technology in teens. But, surprisingly, a rigorous study doesn't bear that out:https://t.co/2tus6qdaOe @NatureHumBehav
— Eric Topol (@EricTopol) January 14, 2019
by @OrbenAmy @UniofOxford @ShuhBillSkee pic.twitter.com/BEIaQZuYak
How to Structure a Data Science Team: Key Models and Roles to Consider - by @AltexSoft https://t.co/Afs1tcYKxT
— Data Science Renee (@BecomingDataSci) January 14, 2019
How to Avoid Being Deceived By Data https://t.co/5kVlEiQavx via @peeplaja
— Data Science Renee (@BecomingDataSci) January 14, 2019
Just for fun, here’s a notebook to visualize how a NN twists and folds space to classify data: https://t.co/5qnufRyUC6 pic.twitter.com/BWUyvjaXvC
— Josh Gordon (@random_forests) January 14, 2019
Interesting thread and responses https://t.co/WwJr0fbSbw
— Thomas G. Dietterich (@tdietterich) January 13, 2019
Fun weekend watch: How the Age of Empires 2 AI thinks. Spent so many hours as a teenager playing against that AI... #gaming #programminghttps://t.co/JFtywn9dgY
— Randy Olson (@randal_olson) January 13, 2019
The generator matters. The perhaps most dangerous form of fake news are statistics/tables/charts used to make things look “scientific” - Statistics are meaningless without a description of how exactly they were derived. The generator.
— Denny Britz (@dennybritz) January 13, 2019
Oh, man. This is why I used to assign @Popehat’s “How to Write a Takedown Request” to students. Guess Bird missed that one. https://t.co/0FGen3fDdQ
— Daphne Keller (@daphnehk) January 12, 2019
Wow, @BirdRide threatening @BoingBoing over @doctorow’s blog post is next-level legal stupidity. https://t.co/GSZ3tNGeF5
— Andy Baio (@waxpancake) January 11, 2019
Alternately, if not a full podcast, let me know if you know of recent episodes of any podcasts that cover these topics. I know there was one with @drewconway on @hugobowne's Data Framed about data science teams:https://t.co/F3y1L8P8VN
— Data Science Renee (@BecomingDataSci) January 11, 2019
I can’t count how many ML engineers and “researchers” I’ve come across who cannot clearly state the hypothesis they are coding, assumptions behind it, and how they are willing to test it, how will they setup a sequence of experiments to learn/debug models and build iteratively.
— Delip Rao (@deliprao) January 11, 2019