Small tip for correctly formatting a collection of Python Notebooks, install @marcwouts 's #jupytext and #black, then run: pic.twitter.com/v4vBQnO3ws
— Martin Renou (@martinRenou) November 23, 2021
Small tip for correctly formatting a collection of Python Notebooks, install @marcwouts 's #jupytext and #black, then run: pic.twitter.com/v4vBQnO3ws
— Martin Renou (@martinRenou) November 23, 2021
We're using https://t.co/GtrlycEDlf and https://t.co/j99uBsAKg2 which are fantastic tools, but there are significant challenges in adapting these tools to account for interference and seasonality in marketplace outcomes.
— Sean J. Taylor (@seanjtaylor) November 20, 2021
An action that automatically compresses AND optimizes images in PRs? Yes, please @calibreapp! https://t.co/tKZ91QcaNP pic.twitter.com/XLCl0a1aIE
— GitHub (@github) November 4, 2021
This blog will examine why distributed training is important and how you can use PyTorch Lightning with Ray to enable multi-node training and automatic cluster configuration with minimal code changes. Read more below:https://t.co/xnpj3A98sv
— PyTorch (@PyTorch) November 2, 2021
Just checking out Hummingbird again for a current project (https://t.co/cmDYE1pbER). It combines my two favorite libraries (scikit-learn & PyTorch) and lets you port over existing models (and leverage GPUs) without having to retrain. Amazing stuff! pic.twitter.com/mwl4Pe0Pmn
— Sebastian Raschka (@rasbt) October 29, 2021
.@Gradio 2.4.0 is out
— AK (@ak92501) October 29, 2021
1. Ability to load Huggingface Spaces as Interfaces
2. Ability to __call__() an Interface as a regular python function
3. Error logging in the front end
4. API docs for every interface
5. Crop Audio
GitHub: https://t.co/u4HDcu3JIz
Breathing K-Means is an interesting extension to K-Means to make it significantly more likely to converge to lower cost minima with a single kmeans++ random init (the manuscript linked in the README has many interesting experimental results): https://t.co/4iJndEGXhH
— Olivier Grisel (@ogrisel) October 19, 2021
New release of dirty_cat ✨: machine learning on dirty categories
— Gael Varoquaux (@GaelVaroquaux) October 13, 2021
The big deal: the SuperVectorizer: easily ingest a (possibly dirty) pandas dataframe in a machine-learning pipeline 🤟https://t.co/mljxVwtnzG
Painless data science directly on the dataframe#pydata pic.twitter.com/m0eM8KjiUE
What if you could predict with your own BERT models in 1ms on GPU and 3ms on CPU, in a Docker container that runs anywhere? Please meet Infinity by @huggingface Please watch https://t.co/uCrCcWPIy0 and sign up for the trial at https://t.co/cdh5b2RowR #MachineLearning #NLP #MLOps
— Julien Simon (@julsimon) October 4, 2021
threadpoolctl 3.0.0 is out: this is a Python utility to introspect & control the number of threads used by OpenMP parallel loops and BLAS linear algebra kernels, typically used in computational libraries (eg NumPy, SciPy and machine learning libraries).https://t.co/TqSb2LXRaJ
— Olivier Grisel (@ogrisel) October 1, 2021
DeepRobust is a PyTorch adversarial library for attack and defense methods on images and graphs. https://t.co/DygJTpo0sF #deeplearning #machinelearning #ml #ai #neuralnetworks #datascience #PyTorch
— PyTorch Best Practices (@PyTorchPractice) September 30, 2021
Here it is! 🔥
— Matt Turck (@mattturck) September 28, 2021
After 100’s of hours of research and writing, excited to release the 2021 ***MAD*** landscape (Machine learning, Artificial Intelligence and Data) - with my colleague @john_d_wu
A crazy, intense, and fun year in the ecosystem
👇👇👇https://t.co/NQDQfaFUk7