Announcing Google-Landmarks-v2, a new dataset with 5M+ images of 200k+ landmarks from across the world, sourced and annotated by the @WikiCommons community. Learn more, including how to take part two associated @Kaggle challenges at ↓ https://t.co/AoqzKW209y
— Google AI (@GoogleAI) May 3, 2019
Didn’t know active volcanos are that common in ImageNet pic.twitter.com/9LX2SGE6eG
— hardmaru (@hardmaru) April 29, 2019
The title of this post is just 💯: https://t.co/NMtgfTryGz
— Miles Brundage (@Miles_Brundage) April 20, 2019
PolyAI is releasing a collection of conversational datasets consisting of hundreds of millions of examples https://t.co/mchj2I4VAD
— Peter Skomoroch (@peteskomoroch) April 16, 2019
My @Stanford lab just released the MRNet dataset of 1000+ annotated knee MRIs. Join our competition to develop and test your knee MRI interpreting deep learning model: https://t.co/1OwfEQuTsY @nbien2 @pranavrajpurkar @RobynLBall @jeremy_irvin16 @curtlanglotz @mattlungrenMD
— Andrew Ng (@AndrewYNg) April 12, 2019
Today we're releasing a large-scale extendable dataset of mathematical questions, for training (and evaluating the abilities of) neural models that can reason algebraically.
— DeepMind (@DeepMindAI) April 3, 2019
Paper: https://t.co/D8g477gcQ4
Code and data: https://t.co/QvR2WkK7j2 pic.twitter.com/EWqNqaOUd5
Can you help detect toxic comments ― and minimize unintended model bias? That's your challenge in our latest Jigsaw competition! Compete for part of a $65,000 prize and improve online conversations for all! Details here 👉 https://t.co/o2P8jCFNTm pic.twitter.com/joGXgChOZ4
— Kaggle (@kaggle) April 2, 2019
Unintended bias is a real challenge in machine learning. We want machine learning to help create a fairer world, not propagate (or even worse, extenuate) current biases.
— Ben Hamner (@benhamner) March 29, 2019
Can you help detect toxic comments *and* minimize unintended bias? https://t.co/wx9u0eitVF
Kaggler @tentotheminus9 creates an auto-updating dataset and kernel [READ] 'Deep Learning Literature with Kaggle and Google Cloud Platform' via @TDataScience 👉 https://t.co/BAe46YR8FR pic.twitter.com/HOgscw3kNR
— Kaggle (@kaggle) March 25, 2019
The Best Public Datasets for #MachineLearning via @rightrelevance thanks @kirkdborne https://t.co/9nDjrFsvLR
— Bojan Tunguz (@tunguz) March 25, 2019
Next and possibly final GPT-2-117M fine-tuning run of the weekend - quotations from this dataset: https://t.co/yOgEkaSsYe
— Miles Brundage (@Miles_Brundage) March 17, 2019
Some GPT-2-117M outputs after fine-tuning on memorable movie quotes from (https://t.co/vZyr8mjNki), using @roadrunning01's Colab. The format of the training data was: [movie title] [line break] [quote] [sometimes another quotes], sometimes with "<p>"/"</p>" thrown in the mix. pic.twitter.com/CjnoAr8L7t
— Miles Brundage (@Miles_Brundage) March 16, 2019
