👾 fun w/ the UpSetR 📦:
— Mara Averick (@dataandme) July 28, 2018
"Hacking our way through UpSetR" 💻 @LIBDrstats https://t.co/noVJ9asR4D #rstats #dataviz pic.twitter.com/UBjHh2qOJq
👾 fun w/ the UpSetR 📦:
— Mara Averick (@dataandme) July 28, 2018
"Hacking our way through UpSetR" 💻 @LIBDrstats https://t.co/noVJ9asR4D #rstats #dataviz pic.twitter.com/UBjHh2qOJq
Very nice in-depth tutorial for NLP transfer learning, including code snippets from the fastai library showing key steps! https://t.co/PCIk9KTfs9
— Jeremy Howard (@jeremyphoward) July 26, 2018
Pandas by Joris Van den Bossche - https://t.co/3nruM5MkWU. This hands-on tutorial will give a basic introduction to pandas, guide you thru the different data structures, explaining the key concepts & defining features. No prior knowledge about pandas is required.
— Python Software (@ThePSF) July 25, 2018
Nice ICML tutorial on imitation learning from @yisongyue and @HoangMinhLe: https://t.co/Amh2R0V94z
— Miles Brundage (@Miles_Brundage) July 23, 2018
The bridge between information theory and machine learning is called: Fano theorem. It begets powerful tools for computing lower bounds for minimax error rates in learning, only using metric quantities (covering number, etc.). Nice tutorial by John Duchi https://t.co/Wk1MrDv1IV pic.twitter.com/bLh0a26BvG
— Elvis Dohmatob (@dohmatobelvis) July 23, 2018
Another good example of tf.keras and eager from @yashk2810, this time for DCGAN: https://t.co/Y18mWehi1Y pic.twitter.com/yTTP7EYRnG
— Josh Gordon (@random_forests) July 21, 2018
Check out this ICML tutorial for a whirlwind tour of current Imitation Learning research: https://t.co/5BWgKuh2vX
— Denny Britz (@dennybritz) July 20, 2018
This is a neat intro to both Gumbel Softmax and GANs in a single post. I like how the full post is written in a Jupyter notebook. https://t.co/GMdJrIGPsi
— Denny Britz (@dennybritz) July 20, 2018
Slides of my talk at #ICML2018 on reproducible #MachineLearning workshop https://t.co/8PCk5cZlnJ https://t.co/cH3PhRZz4K #RML2018 @scikit_learn #opensource #openscience pic.twitter.com/CU22oMgsoS
— Alexandre Gramfort (@agramfort) July 14, 2018
Plant seedling classification: a competition-winning approach using data augmentation and Keras https://t.co/uCFHmriodt
— Ben Hamner (@benhamner) July 13, 2018
Must-watch television: Bayesian Data Science Two Ways: Simulation and Probabilistic Programming, featuring @hugobowne and @ericmjl https://t.co/a6fVcLOOaz
— Chris Fonnesbeck (@fonnesbeck) July 12, 2018
For anyone interested, I'm (mostly done) writing a book which teaches Deep Learning using intuitive examples more than math. All code examples are on Github, written from scratch in Numpy
— Trask (@iamtrask) July 12, 2018
If you think this is your learning style, you can download it here: https://t.co/6KdGghoqLC