Net Runner: new on-device environment for prototyping and evaluating computer vision machine learning models, from @_doc_aihttps://t.co/OHU0QfZFZf
— Jeremy Howard (@jeremyphoward) September 18, 2018
Net Runner: new on-device environment for prototyping and evaluating computer vision machine learning models, from @_doc_aihttps://t.co/OHU0QfZFZf
— Jeremy Howard (@jeremyphoward) September 18, 2018
TIL tqdm now has a new submodule to automatically detect whether you're running the progress bar in a notebook or CLI: from tqdm.autonotebook import tqdm
— Randy Olson (@randal_olson) September 18, 2018
Repo: https://t.co/gr8GVoy08X
Demo of tqdm capabilities: pic.twitter.com/WHhs5utarf
New mypy release: https://t.co/PolzR6FUKg -- Callable protocols and more fixes to overload.
— Guido van Rossum (@gvanrossum) September 17, 2018
ICYMI, peep pkg @bluecology's pkg overview…
— Mara Averick (@dataandme) September 16, 2018
🚗 "Choosing R packages for mixed effects modelling based on the car you drive"https://t.co/R0U5V9vx41 #rstats pic.twitter.com/qGFkgTzFm4
For R projects, you can use `devtools::spell_check` to detect any spelling errors. But what about checking writing style for grammatical (& stylistic) errors?
— Indrajeet Patil (@patilindrajeets) September 15, 2018
`gramr` by @jasdumas has you covered!https://t.co/PWpMoGTa2z
(Reminds me of my 5th-grade English teacher 🤣)#rstats pic.twitter.com/eOuMut0XV3
Lazy man's version: https://t.co/eZdohNWP1l
— Andres Torrubia (@antor) September 15, 2018
Google Cloud Platform now has preconfigured deep learning images with Tensorflow, PyTorch, Jupyter, Cuda and CuDNN already installed. It took me some time to figure out how to start Jupyter on such an instance. Turns out it's a one liner: pic.twitter.com/oTlSZkpCXE
— Martin Görner (@martin_gorner) September 13, 2018
PAIR's new "What-If Tool" lets you probe a machine learning model, no extra coding required! Visualize inference, find counterfactuals, see feature importance, try algorithmic fairness criteria. Now in TensorBoard! @bengiswex @mahimapushkarna Jimbo Wilson https://t.co/7rJ05EXo4X pic.twitter.com/mflXNqCt6b
— Martin Wattenberg (@wattenberg) September 11, 2018
Today, we are launching the What-If Tool, a new feature of the open-source TensorBoard web application, which let users analyze and better understand an ML model without writing code. We look forward to people using, and contributing to, the What-If Tool. https://t.co/s5ivnQH87q
— Google AI (@GoogleAI) September 11, 2018
Democratizing #DeepLearning with The Stanford Dawn Project: https://t.co/8UDYBZq4qn #abdsc #BigData #DataScience #MachineLearning #DataScientists #FeatureEngineering
— Kirk Borne (@KirkDBorne) September 10, 2018
The Dawn Project makes it dramatically easier to build #AI-powered applications. See: https://t.co/hwWK616nVD pic.twitter.com/YKGmdZGj55
Excited to announce the release of another TFX component: @TensorFlow Data Validation -- an open-source library that helps developers understand, validate, and monitor their ML data at scale.
— TensorFlow (@TensorFlow) September 10, 2018
Learn more ↓ https://t.co/seFj1dMkvu
We've updated our online examples to use Dask's new JupyterLab extension.
— Dask (@dask_dev) September 10, 2018
If you want to try out Dask and JupyterLab together, go here:https://t.co/TRjq7gXKbA