Understanding how Anova relates to regression https://t.co/PpiCfYMZGn
— Andrew Gelman (@StatModeling) March 28, 2019
Understanding how Anova relates to regression https://t.co/PpiCfYMZGn
— Andrew Gelman (@StatModeling) March 28, 2019
Sometimes validation loss < training loss. Ever wondered why? 1/5 pic.twitter.com/2FOKQ1kY0w
— Aurélien Geron (@aureliengeron) March 27, 2019
Distributed training for deep learning made easy. As always with slides, code and book chapter. Check out Mu Li's lecture for STAT-157 at https://t.co/gf1fXEZafC #D2L #Gluon
— Alex Smola (@smolix) March 26, 2019
Book recommendations from @ykanellopoulos on AI ethics include @ndiakopoulos @Soccermatics @mathbabedotorg @merbroussard @FrankPasquale #StrataData pic.twitter.com/fnZHJVUC9J
— Rachel Thomas (@math_rachel) March 26, 2019
'Tips and Tricks for Machine Learning' a live presentation from #KaggleDays Paris by Kaggle Grandmaster Stanislav Semenov. [WATCH] https://t.co/yOoVKPL0Z0 // @Kaggle_Days pic.twitter.com/UquzC4Df4Z
— Kaggle (@kaggle) March 26, 2019
👍 code-through w/ nice viz, too!
— Mara Averick (@dataandme) March 25, 2019
"awtools Update: Visualizing Natural Disaster Cost" 👨💻 @awhstinhttps://t.co/daJD2DLZur #rstats #dataviz pic.twitter.com/sTwWKECjTX
Visualizing memorization in RNNs — A new Distill article by @andreas_madsen.https://t.co/urqxBDxiME
— distillpub (@distillpub) March 25, 2019
Just finished rewriting the ConvNet chapter! 😅
— Aurélien Geron (@aureliengeron) March 24, 2019
Now includes building ResNet-34 in #TensorFlow 2 (see image), fine-tuning a pretrained model, object detection and image segmentation.
I pushed the notebook: https://t.co/YEkeSzGT1V pic.twitter.com/eNt0YhxqIf
Keras in TensorFlow 2.0 has a new developer guide, with lots of useful content for power users: https://t.co/Dzrl15ftto
— François Chollet (@fchollet) March 22, 2019
Here is the recording of the talk about the callback system in fastai I gave yesterday @TwoSigmaVC hosted by @Cometml for the @PyTorch meetup in NYC. My part begins at 7:32.https://t.co/LoZsEbViZH
— Sylvain Gugger (@GuggerSylvain) March 21, 2019
Checklist for debugging neural networks --
— Sebastian Raschka (@rasbt) March 20, 2019
Tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models https://t.co/fo1GfxGInW
"How to do FastAI even faster: Speed up preprocessing using fastai’s built in ‘parallel’ function"
— Jeremy Howard (@jeremyphoward) March 20, 2019
Nice introduction to a handy little helper function.https://t.co/buji9oTsLj pic.twitter.com/bMx9Hrh62w