What are pretrained models and why are they useful?
— Radek Osmulski (@radekosmulski) November 22, 2018
✅overview (vision, NLP)
✅using a model for classification
✅training our own chewbacca vs yoda classifierhttps://t.co/SsdVMtsNzU
What are pretrained models and why are they useful?
— Radek Osmulski (@radekosmulski) November 22, 2018
✅overview (vision, NLP)
✅using a model for classification
✅training our own chewbacca vs yoda classifierhttps://t.co/SsdVMtsNzU
How Etsy Handles “Peeking” in A/B Testing by Kelly Shen - https://t.co/dBbkRGVn41. This talk covers how Etsy utilizes Python to investigate and evaluate one of the problems, “peeking” at results early in order to detect maximum significance with minimum sample size.
— Python Software (@ThePSF) November 18, 2018
Understanding optimization (convex and nonconvex) in #DeepLearning and other gradient descent applications : https://t.co/m3YjJ2S9EI #BigData #MachineLearning #AI #DataScience
— Kirk Borne (@KirkDBorne) November 18, 2018
For example: https://t.co/JAO62EJ0nc pic.twitter.com/kr8FkAKqTa
Btw. I forgot to mention that I had also made a single PDF of all 4 model evaluation articles, which is maybe easier to read (and/or if you prefer print versions) https://t.co/BDuQ1RBjD0 https://t.co/7GQ5cIMwJ5
— Sebastian Raschka (@rasbt) November 15, 2018
Want to clarify that my previous tweet contains a "wrong" figure. Had updated it some time ago https://t.co/8P9wdBf7Wc but somehow forgot to update the jpg version as well when I dragged into the tweet (because twitter doesn't allow sharing PDFs) :P pic.twitter.com/FVUj4eg6gW
— Sebastian Raschka (@rasbt) November 11, 2018
BlackBox NLP 2018 slides: https://t.co/DL0smjHchA
— (((ل()(ل() 'yoav)))) (@yoavgo) November 5, 2018
New blog post on "The Three Ds of Machine Learning Systems Design" https://t.co/sfIoszOIzJ
— Neil Lawrence (@lawrennd) November 5, 2018
"The Nuts and Bolts of Deep RL Research," John Schulman: https://t.co/PE4K3p3t9Z
— Miles Brundage (@Miles_Brundage) October 31, 2018
Lots of very useful tips here!
There's a nice blog post today describing AdaNet, one project in our overall efforts in the AutoML research area, along with accompanying open source release and tutorial notebooks (see last paragraph of the blog post for links to these):https://t.co/mnfP2bGSao
— Jeff Dean (@JeffDean) October 30, 2018
The Five Major Objectives of the M4 Forecasting Conference:
— Spyros Makridakis (@spyrosmakrid) October 27, 2018
New York CIty, December 10 and 11 https://t.co/dC6vpmRjBC
For information about the comparisons of ML and Stat forecasting methods for the M3 Competition https://t.co/S3BpRgtxUW
for the M4 https://t.co/fYp0Mn2fCn pic.twitter.com/I74LnHOOUI
And the slides are available at https://t.co/nWafl9SvuG https://t.co/DORWnmd6Eq
— Adam Paszke (@apaszke) October 25, 2018
new blog post: understanding multinomial regression with partial dependence plots https://t.co/8lXH2HBNR1
— alex hayes (@alexpghayes) October 24, 2018
targeted at #rstats users starting to explore non-linear models! pic.twitter.com/kTbbklngQB