Neat dataset to explore the impact of things like weather and time-of-day on Uber and Lyft prices in Boston: https://t.co/MIejdDvVC0
— Anthony Goldbloom (@antgoldbloom) June 11, 2019
Neat dataset to explore the impact of things like weather and time-of-day on Uber and Lyft prices in Boston: https://t.co/MIejdDvVC0
— Anthony Goldbloom (@antgoldbloom) June 11, 2019
Read @lavanyaai's awesome Kernel detailing how she climbed the competition leaderboard | "How I made top 0.3% on Kaggle" 👉📈 https://t.co/e0hQhOtAW1
— Kaggle (@kaggle) June 10, 2019
Having spent a lot of time with both naive bayes and fine tuned neural nets, I thought this seemed wrong, so I ask the https://t.co/GEOZuodrZj community to check it out.
— Jeremy Howard (@jeremyphoward) June 6, 2019
At this stage, ULMFiT neural nets are winning the humor recognition competition: https://t.co/FJsEBbO6co https://t.co/YOQIXAZe1G
Here's a little table you might find handy if you're wondering which language wikipedias are likely to be useful for language model pre-training, e.g. for ULMFiT. It's sorted by "depth" (see link for definition) multiplied by number of articles.https://t.co/FOO2HvCH9v pic.twitter.com/TuKAJFaAUO
— Jeremy Howard (@jeremyphoward) June 5, 2019
kedro - A Python library for building robust production-ready data and analytics pipelines https://t.co/9DvjIluQ8t
— Python Trending (@pythontrending) June 4, 2019
Here's regression example on the Ames Housing Price dataset. This dataset turns out to be great for demonstrating how to vectorize structured data, and how to handle missing features. https://t.co/aEQDn38psw
— François Chollet (@fchollet) June 3, 2019
Thanks to @micahjsmith for suggesting this dataset!
The Fashion IQ Dataset: Retrieving Images by Combining Side Information and Relative Natu... https://t.co/X1GJGU8YuA pic.twitter.com/j3M4jjHrLl
— arxiv (@arxiv_org) June 3, 2019
Advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.https://t.co/4X29r9x2Fg pic.twitter.com/8WOfRF0kY9
— ML Review (@ml_review) June 2, 2019
https://t.co/niU2rXKXB8 great to have one standardized dataset with one leaderboard for molecular property prediction
— Kyunghyun Cho (@kchonyc) June 2, 2019
Use your data science smarts to make big predictions at a molecular level! ⚛ In our newest competition you'll measure the magnetic interactions between a pair of atoms for a chance to win part of a $30,000 prize. Get started now: https://t.co/bxJzCAPu4d
— Kaggle (@kaggle) May 29, 2019
Kmnist Benchmark japanese handwriting recognition competition: $1000 in compute credits to the contributor of the highest validation accuracy by July 8. Kuzushiji-MNIST is a drop-in replacement for the MNIST dataset (28x28 grayscale, 70,000 images) https://t.co/UuPvVKdMnF
— Peter Skomoroch (@peteskomoroch) May 28, 2019
I like this GPT-2 post update: Data release for detection research, Bigger (117M -> 345M) model release for your creative works. Nice follow up from @OpenAI! https://t.co/kGKmk9XymY
— Delip Rao (@deliprao) May 4, 2019