— Vladimir Iglovikov (@viglovikov) March 14, 2020
— Vladimir Iglovikov (@viglovikov) March 14, 2020
Want to say thank you to the maintainers of Papermill
— Hamel Husain (@HamelHusain) March 13, 2020
cc: @WillingCarol @mxpacer @codeseal
You can update Notebooks in GitHub Actions Automatically, and push that to GitHub Pages, which is what we are using to update COVD-19 stats on https://t.co/iOmrLjwQnd . Super cool. pic.twitter.com/FkZnPLaeAa
Neural Tangents is an open source library we've been working on to make it easy to build, train, and manipulate infinitely wide neural networks.
— Sam Schoenholz (@sschoenholz) March 13, 2020
To appear as a spotlight at ICLR.
code: https://t.co/Ic35RS52Z2
paper: https://t.co/2KqBv44KJt
colab: https://t.co/c8yWJxOJfg https://t.co/LFRLLRCqLw
If you want to go really deep on this, check out Stan, which is a whole language for probabilistic programming. Here's some survival models for Stan:https://t.co/DPFLm2bbzO
— Jeremy Howard (@jeremyphoward) March 12, 2020
ForwardTacotron - a simplified Tacotron without attention for Speech Synthesis, efficient, fast and robust.
— PyTorch (@PyTorch) March 10, 2020
🔈 Samples: https://t.co/xMq58HMQuC
🔤 Github: https://t.co/5Ew7u7AA2P
📕 Colab: https://t.co/uK1Qle1kwK https://t.co/x4q41lzEJh
We just published 2 packages: MediaPipe Facemesh and Handpose in the TensorFlow.js models repo!
— Nikhil Thorat (@nsthorat) March 9, 2020
Facemesh & Handpose give 3D keypoints from images of faces and hands respectively, with an API free of ML jargon. They also run in WASM! https://t.co/RubnKeQjWm
Historically, it has been very difficult for researchers to write quantum deep learning models. This new package makes it an order of magnitude easier. pic.twitter.com/OUDCyuFbPj
— François Chollet (@fchollet) March 9, 2020
A new, official Dask API for XGBoost by Jiaming Yuan https://t.co/dt2k4jEhwO
— Bojan Tunguz (@tunguz) March 9, 2020
Announcing TensorFlow Quantum (#TFQ), an open-source library for the rapid prototyping of quantum #MachineLearning models, bringing the quantum computing and ML research communities together to accelerate the discovery of new quantum algorithms. https://t.co/y624QyVjV8
— Google AI (@GoogleAI) March 9, 2020
Private Fastpages On Kubernetes:
— Jeremy Howard (@jeremyphoward) March 8, 2020
"How I deployed FastPages as a tool for our data tribe to share information and insights on the private Kubernetes cluster at my workplace"https://t.co/P1OSCtLSSK
Props to A. K. Subramanian for putting out this reference library of variational autoencoder implementations. This is a fabulous resource. https://t.co/igsgsnUTSX pic.twitter.com/mKfAn4qZAi
— Brandon Rohrer (@_brohrer_) March 6, 2020
I'm blown away by just how comprehensive the `performance::check_model` is! 🤯https://t.co/f7NnPPKAJ3
— Indrajeet Patil (@patilindrajeets) March 3, 2020
Enter the model object & it checks for:
✅ normality of residuals
✅ normality of random effects
✅ heteroscedasticity
✅ homogeneity of variance
✅ multicollinearity#rstats pic.twitter.com/u7fbFFDbYO