I hope you'll enjoy this step-by-step #notebook tutorial
— Andrew Trask (@iamtrask) September 30, 2020
Goal: to be the *easiest* intro to #privacy preserving Deep Learning available
It's in @PyTorch teaching the #PySyft PPML framework
I hope you enjoy it#GDPR #100DaysOfMLCode #100DaysOfCodehttps://t.co/zUORRBt8aS pic.twitter.com/a4tADBc9B1
Want to learn how to use TensorFlow to create TFRecords for Tensor Processing Units? Check out this notebook walkthrough from Kaggle AI Educator @ryanpholbrook! [WATCH] https://t.co/PEPU5ea5as pic.twitter.com/jXdjukGotZ
— Kaggle (@kaggle) September 29, 2020
I wrote a short tutorial explaining three different methods for training self-play agents (tournament-style genetic algorithms, self-play PPO, and self-play via cooperation) using Slime Volleyball Gym as an example environment.https://t.co/7rxqW0vLnb https://t.co/j0HWLQuWza pic.twitter.com/L7zVFjN2wx
— hardmaru (@hardmaru) September 24, 2020
Whether you're a football/soccer fan or not, this is a great walk-through of basic class construction, validation, etc…
— Mara Averick (@dataandme) September 23, 2020
⚽️ “S3 Classes and {vctrs} to Create a Soccer Pitch Control Model” by @TonyElHabr https://t.co/6VIEno8n3l #rstats
New tutorial!🚀
— Adrian Rosebrock (@PyImageSearch) September 21, 2020
Intro to Automatic License/Number Plate Recognition with #OpenCV, #Python, and #DeepLearning 🚗🚚
- Detect license plates with OpenCV
- Extract characters
- #OCR license platehttps://t.co/SJyeME7sfV 👍#MachineLearning #ArtificialInteligence #AI #DataScience pic.twitter.com/UQk4Oq7m4V
Reading HTML tables with Pandas - Practical Business Python https://t.co/r9bqItmSKs
— PyCoder’s Weekly (@pycoders) September 19, 2020
Want to know everything you can do with your tests in Python (repeating, randomizing, selecting, skipping, running only the tests for the files you locally changed and more)? @StasBekman just wrote a very comprehensive guide that we follow ourselves! https://t.co/UvaLozV6UU
— Hugging Face (@huggingface) September 16, 2020
The full hands-on tutorials, "Building Recommender Systems with PyTorch", are now available. We show how to build deep learning recommendation system and resolve the associated interpretability, integrity, and privacy challenges. See: https://t.co/vaqkdhpvyr
— PyTorch (@PyTorch) September 10, 2020
With @rapidsai, @huggingface, @PyTorch & @dask_dev we show how to scale state of the art #NLP. #strongertogether! Over 2x faster than prior #GPU implementations, 450x faster subword tokenizer, & most importantly better accuracy. Learn more https://t.co/onS8t75vPk & try it today!
— RAPIDS AI (@rapidsai) September 9, 2020
New blog post: PCA tidyverse style.
— Claus Wilke (@ClausWilke) September 8, 2020
I've been struggling with doing PCA using an idiomatic tidyverse approach. Now I think I've figured it out.#rstats #tidyverse https://t.co/mRNJx7O2t6
Catalyst is a PyTorch library that helps you write DL pipelines with a few lines of code. It now offers metric learning, enabling feature extractor by mapping objects from the same class into the nearest points and distancing points for different classes. https://t.co/UDQLRJxrBk
— PyTorch (@PyTorch) September 2, 2020
New code example on https://t.co/m6mT8SrKDD: probability distribution density estimation using RealNVP https://t.co/K5cfQTVPBy
— François Chollet (@fchollet) September 1, 2020
