Hand-labeling training data for machine learning problems is effective, but very labor and time intensive. This work explores how to use algorithmic labeling systems relying on other sources of knowledge that can provide many more labels but which are noisy. https://t.co/7N8W4h869X
— Jeff Dean (@JeffDean) March 14, 2019
Game on. Let’s roll data scientists. Let me know what you find out. @LX_CW kudos to you and the team. This isn’t easy to do 🇺🇸 https://t.co/AXzlWuXoNW
— dj patil (@dpatil) March 14, 2019
Imagenette too easy for you? Then try your hand at Imagewoof - just like Imagenette, but with more woof! Creating a doggie classifier from scratch with just 1000 images won't be easy...
— Jeremy Howard (@jeremyphoward) March 7, 2019
If you try it out, let me know! (But don't use a pretrained model.)https://t.co/joqArlFxvC pic.twitter.com/FsCv87qLjk
Help underserved youth get career advice by matching their questions with professionals in the field. Thanks to our friends at @careervillage for hosting this Data Science for Good competition. $15,000 and Kaggle swag is on the line. Get started now! https://t.co/X6ib8IaxdR
— Kaggle (@kaggle) March 6, 2019
Presenting Imagenette. A smaller subset of 10 easily classified classes from Imagenet, and a little more French.
— Jeremy Howard (@jeremyphoward) March 6, 2019
I've found this quite helpful in my research and will be using it in our teaching. I hope you find it helpful too! :)https://t.co/GFUJ9a5QTk pic.twitter.com/fKtPPhIbei
Google released their 32G STORIES corpus https://t.co/1btLnNq99h
— Delip Rao (@deliprao) February 28, 2019
Finally, there will be open source efforts to replicate this. Why? One simply cannot hold back innovation and embargoes don’t work in low cost of entry technologies. https://t.co/kibwaf4vGL
— Delip Rao (@deliprao) February 28, 2019
So many alternatives emerging to the withheld WebText corpus referenced in the GPT-2 paper. Start with this one and build datasets bigger and bolder than WebText. Just a few more examples ... https://t.co/SVO35pY1iR
— Delip Rao (@deliprao) February 28, 2019
Another comment on the GPT-2 data: the WMT 2019 training data this year for English-German consists of 28GB of English and 58GB(!!!) of German plain text news data with document boundaries. So, similar to @OpenAI Webtext, news-domain but bilingual: https://t.co/EHOD3ZvGL7
— Marian NMT (@marian_nmt) February 27, 2019
Pretty cool how StyleGAN can generate entire fonts in one shot. https://t.co/Jiv0eY0E7I
— hardmaru (@hardmaru) February 13, 2019
Heading into the weekend... with TWO new Playground competitions!
— Kaggle (@kaggle) February 8, 2019
☝️Movie buffs, rejoice! 🍿Box Office Prediction is for you: https://t.co/LOlCTSzQEi
✌️A sequel to our weirdest comp ever: Don't Overfit! https://t.co/3rEoOSbIFQ
Compete for Kaggle SWAG 👇 and bragging rights! pic.twitter.com/b5LGyoAiyW
Can you help end gender bias in pronoun resolution? 🙅👫 Find out in our latest research competition! Just launched: Gendered Pronoun Resolution hosted by @GoogleAI | Get started today! 👉 https://t.co/fewDygzy9L pic.twitter.com/6pa37tOe6f
— Kaggle (@kaggle) February 7, 2019
