Watch NVIDIA's David Nola explain how NVIDIA Clara Toolkits make building AI easy by providing tools that make data annotation, training and deployment seamless for medical imaging applications.
— NVIDIA AI (@NvidiaAI) August 30, 2019
Watch NVIDIA's David Nola explain how NVIDIA Clara Toolkits make building AI easy by providing tools that make data annotation, training and deployment seamless for medical imaging applications.
— NVIDIA AI (@NvidiaAI) August 30, 2019
New Forest Inference Library (FIL) just released! Accelerate @XGBoostProject, LightGBM and Random Forest inference with @NVIDIA #GPUs - 30x faster than on CPUs. https://t.co/0x4vLkkq4B #machinelearning pic.twitter.com/bZ6QIECNM6
— RAPIDS AI (@rapidsai) August 29, 2019
TensorFlow 2.0.0-rc0 has been released! With a focus on simplicity and ease of use, this release features API simplification, easy model building with Keras, and more.
— TensorFlow (@TensorFlow) August 29, 2019
See the full release notes for details on added features and changes ↓ https://t.co/UNdjms70MC
Can someone please go to @DeepMindAI and give the person behind the OpenSpiel 'phase portraits' an "accidental AI paper art" prize? These are fantastic!
— Jack Clark (@jackclarkSF) August 29, 2019
Read more: OpenSpiel: A Framework for Reinforcement Learning in Games https://t.co/5PzTq6IOhO pic.twitter.com/xov5wPea6q
The team blew me away with this release. I spent years in #CyberSecurity wanting to do pagerank this fast. cuDF is approaching ludicrous speed... and cuML’s scale is a game-changer then they added inference functionality... If you haven’t tried @RAPIDSai, you’re missing out. https://t.co/i8i48aGnPx
— Joshua Patterson (@datametrician) August 28, 2019
👨🏫 {tidylog} can be make great teaching tool (now w/ joins, too!)
— Mara Averick (@dataandme) August 28, 2019
📦 "tidylog: Logging for 'dplyr' Functions" by @elbersbhttps://t.co/V5VWgjEiPH #rstats
🎥 screencast demo by @oscar_b123 https://t.co/3U2yM3xf0D pic.twitter.com/5jiQdic5WW
We're excited to release OpenSpiel: a framework for reinforcement learning in games. It contains over 25 games, and 20 algorithms, including tools for visualisation and evaluation.
— DeepMind (@DeepMindAI) August 27, 2019
GitHub: https://t.co/RKMqy3olet
Paper: https://t.co/gVaCu7PCLQ pic.twitter.com/9atJDrpHHw
Facebook AI researchers are releasing a new feature for the fastText library which provides hyper-parameter autotuning for more efficient text classifiers. https://t.co/gINIHhViTO pic.twitter.com/TV84W4skav
— Facebook AI (@facebookai) August 26, 2019
The @fastdotai community has been hard at work trying to make sense of the new state of the art in optimizers. Towards this end @mgrankin put together this repo with results + implementations! I'll definitely be trying this. https://t.co/3tN9CJDqI4 pic.twitter.com/wfKd8MWp57
— Jason Antic (@citnaj) August 25, 2019
Watch "Solving the model representation problem with broom" with Alex Hayes from rstudio::conf(2019)
— RStudio (@rstudio) August 24, 2019
🎦 https://t.co/6axMVfJ1Ab
Learn more about and register for rstudio::conf(2020) in San Francisco at https://t.co/rYJqkzCywm #rstats #DataScience
This replication project trained a 1.5B parameter “OpenGPT-2” model on OpenWebTextCorpus, a 38GB dataset similar to the original, and showed comparable results to original GPT-2 on various benchmarks. 👏🏼https://t.co/m4ZMB8RmdShttps://t.co/ZrqJ0IuHbw https://t.co/o3KBv5VXKJ pic.twitter.com/pGN0p00DBR
— hardmaru (@hardmaru) August 23, 2019
‼️ 1.5B parameter GPT-2 model released, but not by OpenAI https://t.co/8tgjUWxjZo
— Mark 🦑. Riedl (@mark_riedl) August 22, 2019