Computing Receptive Fields of Convolutional Neural Networks -- A new Distill article by André Araujo, Wade Norris, and Jack Sim.https://t.co/XqvDBlNBoM
— distillpub (@distillpub) November 4, 2019
Computing Receptive Fields of Convolutional Neural Networks -- A new Distill article by André Araujo, Wade Norris, and Jack Sim.https://t.co/XqvDBlNBoM
— distillpub (@distillpub) November 4, 2019
New tutorial!🚀 Traffic Sign Classification with #Keras and #TensorFlow 2.0 🛑🚦⚠️
— Adrian Rosebrock (@PyImageSearch) November 4, 2019
- 95% accurate
- Includes pre-trained model
- Full tutorial w/ #Python codehttps://t.co/MkWiTaYKwU 👍#DeepLearning #MachineLearning #ArtificialIntelligence #DataScience #AI pic.twitter.com/20jYvQw7Dw
My talk on "Getting Specific about Algorithmic Bias"https://t.co/K5MOawggb4 pic.twitter.com/efNQtoVClJ
— Rachel Thomas (@math_rachel) November 3, 2019
Enjoyed this talk by @andrewgwils and his students (@polkirichenko & @Pavel_Izmailov) introducing and motivating their recent work on Stochastic Weight Averaging.
— Zachary Lipton (@zacharylipton) November 3, 2019
Papers:
SWA: https://t.co/zRPuRDmCLk
Mode Connectivity: https://t.co/eCUPbQG4WChttps://t.co/D1pGnkvxw8
Must-read paper for folks working on experimentation. Great intuition, interesting results, and practical advice that you probably haven’t heard already. https://t.co/RPwyyDnu7p
— Sean J. Taylor (@seanjtaylor) November 2, 2019
OMG Just found this amazing extension of my sklearn cheat-sheet by @chris_bour:
— Andreas Mueller (@amuellerml) November 1, 2019
https://t.co/T5vZ1I5g13 pic.twitter.com/bbwRfiixif
In this #tidytuesday screencast, I analyze a dataset of squirrels in Central Park🐿️🐿️🐿️
— David Robinson (@drob) November 1, 2019
This includes a visualization from NYC shapefiles, as well as a quick Shiny app to explore how squirrel behavior varies across the park!https://t.co/uXxrdgzaUQ #rstats pic.twitter.com/TlGCpqHqGd
See a new example combining Dask and PyTorch for scalable batch prediction.https://t.co/9aB6cU2f5A
— Dask (@dask_dev) October 31, 2019
Watch "Data science as a team sport" with Angela Bassa from rstudio::conf(2019)
— RStudio (@rstudio) October 31, 2019
🎦 https://t.co/g398W5iW4C
Learn more about and register for rstudio::conf(2020) in San Francisco at https://t.co/rYJqkzCywm #rstudioconf #rstats #DataScience
We are so pleased to share this fantastic result from @RecursionPharma ☺️👇RT if you competed in this competition 👏 https://t.co/JXwVmloBDm
— Kaggle (@kaggle) October 30, 2019
Some people asked about resources/tips on debugging machine learning models. Here you go :-)
— Chip Huyen (@chipro) October 30, 2019
1. https://t.co/9Y7kDc1hag
2. https://t.co/6P6GJQIHG3
3. https://t.co/j2lUKTTfqF
4. https://t.co/qJrLtwMBWm
5. https://t.co/qru8lLkILy
6. https://t.co/5KUjp5XD6E https://t.co/uOKr2VmjNV
This is a wonderful paper that should be read by every ML researcher concerned with explainability. I dont know how it escape my attention. I would shorten it more, skip the "according to so and so.." and present ML folks with a computer-minded taxonomy of explanation #Bookofwhy https://t.co/UGvL1YYV0W
— Judea Pearl (@yudapearl) October 30, 2019