Competition launch: Steel Defect Detection 🛠⚙ Can you help @Severstal_PAO engineers improve their algorithm by localizing and classifying surface defects on a steel sheet? If so, you could claim part of a $120,000 prize. Learn more and start today 👉 https://t.co/7xxMZ9Gc3x pic.twitter.com/YGpx8yhmbg
— Kaggle (@kaggle) July 26, 2019
Looking for a good small dataset for work? Check out this thread and this blog post I put together a while back. https://t.co/5hd0VVgiht
— Vicki Boykis (@vboykis) July 24, 2019
This looks like an amazing dataset. Can experts please comment on it. Thanks https://t.co/1CGf5kaDpU
— Nando de Freitas (@NandoDF) July 24, 2019
Great original post by @Datawrapper's @lisacrost & 👍🏼 engagement (https://t.co/NnlIkiZ7cB) from @TheEconomist's @AlexSelbyB.
— boB 🇷udis (@hrbrmstr) July 16, 2019
Worth a complete read to grok just how hard this "communicating with data" can be.
They were awesome enough to share the data, too (ref'd in the post). https://t.co/kiqX0wbp8C pic.twitter.com/YAj1pvJQv3
Learn how @shivamshaz approached (and won!) our Data Science for Good Challenge and helped improved the City of Los Angeles' recruitment pipeline. READ: https://t.co/PHXLN2fjFB #winnerinterview #nofreehunch #datascienceforgood pic.twitter.com/df063SHcWc
— Kaggle (@kaggle) July 15, 2019
Kuzushiji-MNIST, Kuzushiji-49, and Kuzushiji-Kanji datasets https://t.co/PQ9ihH9ivG https://t.co/jHazI8xSGh
— hardmaru (@hardmaru) July 9, 2019
Think you can build a model to detect specific themes and topics within a YouTube video— and pinpoint the exact time they occur? That's what competitors in our 3rd YouTube-8M Video Detection Challenge aim to do! Learn more and join the challenge today: https://t.co/VQzh9zdmit pic.twitter.com/PDOXK6JX54
— Kaggle (@kaggle) July 3, 2019
We are happy to announce the updated YouTube-8M dataset, which includes a subset with verified 5-s segment level labels, along with the 3rd Large-Scale Video Understanding Challenge and Workshop at @ICCV19. Join us and help build on last year’s success! https://t.co/xjOxWSdBbl
— Google AI (@GoogleAI) June 28, 2019
Wikipedia has a handy API for accessing data! Here’s how to load, parse and visualize a data table in Observable using Vega-Lite. https://t.co/6HlBnhbRZ0
— Mike Bostock (@mbostock) June 25, 2019
population pyramids galore for #plotswithchristine
— Christine Zhang (@christinezhang) June 22, 2019
i noted here https://t.co/ljKDGXjjTj that DC's white population has an extreme 25-34yo bulge. compare that w/other races:
data&code: https://t.co/jS6eGJIHry, using #rstats+#python ⚡️ (median interpolation function ht @palewire) pic.twitter.com/QtKWBORo4F
ABC: Free Datasets for Geometric Deep Learning – AI³ | Theory, Practice, Business – Medium https://t.co/hYoJFeY9J1 #AI #MachineLearning #DeepLearning #DataScience
— Mike Tamir, PhD (@MikeTamir) June 17, 2019
"How does the Bay Area Commute?" @en_dash and I shared the data from our https://t.co/6a2kNgJyz5 project at the start on @figshare - it's also on @kaggle and @jonobate did this very cool analysis of Bay Area commutes with it, great write up https://t.co/m4wMkSVre1 #DataScience pic.twitter.com/1Q87DEtCOR
— Alasdair Rae (@undertheraedar) June 12, 2019
