A friend asked for my fave data science books & I figured y'all might find the list useful as well. 🤓📚 https://t.co/g7CfeDI7iu
— Rachael Tatman (@rctatman) December 4, 2018
A friend asked for my fave data science books & I figured y'all might find the list useful as well. 🤓📚 https://t.co/g7CfeDI7iu
— Rachael Tatman (@rctatman) December 4, 2018
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction by Leland McInnes. https://t.co/nSt7USWsXv. This talk presents a new approach to dimension reduction called UMAP.
— Python Software (@ThePSF) December 3, 2018
"How to visualize decision trees". Great talk by @the_antlr_guy - inspiring level of thoughtful design that is not often seen in software development https://t.co/00uEAk0LKg
— Jeremy Howard (@jeremyphoward) December 3, 2018
The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)https://t.co/cc5EQ9Xa7b
— Jay Alammar جهاد العمار (@jalammar) December 3, 2018
A visual look at some of the leading breakthroughs in NLP in 2018. These models from @ai2_allennlp @seb_ruder @jeremyphoward @OpenAI @GoogleAI started a new NLP era!
New Blog post! pic.twitter.com/HSCWK6WhiB
— François Chollet (@fchollet) December 2, 2018
ICYMI, dig this visual intro to the infer 📦 verbs…
— Mara Averick (@dataandme) December 2, 2018
🌲 "Statistical Inference: A Tidy Approach" by @old_man_chester https://t.co/vEtDotrWcf #rstats ht @LVaudor pic.twitter.com/4JJHSclz2i
A Programmer's Introduction to Mathematics: https://t.co/lfv8QJhC0t - This book looks amazing. Learning pure theory has always been difficult for me, but once you add real-world examples and implementations the concepts usually become intuitive and clear.
— Denny Britz (@dennybritz) December 2, 2018
Maths behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs’ training https://t.co/tE12kPyXiV #AI #MachineLearning #DeepLearning #DataScience pic.twitter.com/OFPDPVeWlB
— Mike Tamir, PhD (@MikeTamir) December 2, 2018
The style transfer example created by Raymond last July is a great example of the ease of use of eager execution combined with the Keras API: https://t.co/fM0NR1ZINX
— François Chollet (@fchollet) December 1, 2018
🎰 @mjskay also has a great collection of visualization "uncertainty examples" w/ #rstats code: https://t.co/OxE9iKHPpb#dataviz #infovis https://t.co/gROHjtFbS4
— Mara Averick (@dataandme) December 1, 2018
Stephen Wolfram explains neural nets https://t.co/mShchBW3yU
— Andrew Gelman (@StatModeling) November 30, 2018
😢 Missed @opencpu's talk? @rOpenSci's got you covered…
— Mara Averick (@dataandme) November 29, 2018
📸 "Working w/ images in R" #rstats
🎬 vide: https://t.co/TYZ2YQxnVe
📽 slides: https://t.co/QZCLKMgmEM
📝 collaborative notes: https://t.co/QZCLKMgmEM pic.twitter.com/HBkYhWTv3s