My original comment was meant for a separate HN article on machine learning and I posted in the wrong tab. My apologies.
How is this related to the article on gengo.ai?
Oops, missread for https://modelzoo.co/
Datasets for Machine Learning
11–20 of 41 posts
Re: Datasets for Machine Learning
#12Ben from Kaggle. Open up the ~50 different individual datasets linked in separate tabs, and then quickly flip through all of them trying to get a sense of what each one is. That experience will demonstrate one of the main challenges we're aiming to solve by making Kaggle Datasets your default place to publish data online ( https://www.kaggle.com/datasets )
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
Re: Datasets for Machine Learning
#13For audio, LibriSpeech, M-AILABS, LJ-Speech, VCTK, TIMIT, Mocha-Timit, VoxForge, Blizzard Challenge, and so on.
Re: Datasets for Machine Learning
#14Ben from Kaggle. Open up the ~50 different individual datasets linked in separate tabs, and then quickly flip through all of them trying to get a sense of what each one is. That experience will demonstrate one of the main challenges we're aiming to solve by making Kaggle Datasets your default place to publish data online ( https://www.kaggle.com/datasets )
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
(Not Ben, but - ) outside of academia, the main thing that seems to encourage people to do supervised ML is that it's the only thing that seems to work. I haven't really heard of any success stories with using unsupervised techniques for most common ML applications.
Re: Datasets for Machine Learning
#15Ben from Kaggle. Open up the ~50 different individual datasets linked in separate tabs, and then quickly flip through all of them trying to get a sense of what each one is. That experience will demonstrate one of the main challenges we're aiming to solve by making Kaggle Datasets your default place to publish data online ( https://www.kaggle.com/datasets )
This is great! Thanks for sharing. Would be awesome if your license filter had a "not for commercial use" vs. "for commercial use" or similar.
Re: Datasets for Machine Learning
#16Earlier quoted context omitted.
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
(Not Ben, but - ) outside of academia, the main thing that seems to encourage people to do supervised ML is that it's the only thing that seems to work. I haven't really heard of any success stories with using unsupervised techniques for most common ML applications.
What about clustering?
Re: Datasets for Machine Learning
#17Earlier quoted context omitted.
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
(Not Ben, but - ) outside of academia, the main thing that seems to encourage people to do supervised ML is that it's the only thing that seems to work. I haven't really heard of any success stories with using unsupervised techniques for most common ML applications.
I used a very simple unsupervised ML built in scikit-learn to find good matches on OK Cupid. Worked very well, it found definite boundaries between the clusters of women.
One of the features was a subjective rating of how much I liked some of the women, and scikit-learn then suggested to me other women in the clusters that had my best ratings. It turns out that I like vegetarians, redheads, and left-wingers. Which happens to be true, even though I eat meat and do not identify as left-wing. But those traits correlate with _other_ traits that are more difficult to measure objectively, such as caring about children, liking to hike, and preferring an evening of sex to an evening of television.
Re: Datasets for Machine Learning
#18Earlier quoted context omitted.
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
(Not Ben, but - ) outside of academia, the main thing that seems to encourage people to do supervised ML is that it's the only thing that seems to work. I haven't really heard of any success stories with using unsupervised techniques for most common ML applications.
Unsupervised works, but your ability to measure "does it work or not" is much more dependent on a case by case evaluation rather than a score.
(Because if you know a priori what is it that you want to measure - it's supervised)
Re: Datasets for Machine Learning
#19Ben from Kaggle. Open up the ~50 different individual datasets linked in separate tabs, and then quickly flip through all of them trying to get a sense of what each one is. That experience will demonstrate one of the main challenges we're aiming to solve by making Kaggle Datasets your default place to publish data online ( https://www.kaggle.com/datasets )
I've heard that Kaggle data sets encourage people to do "supervised" ML only. Is that true?
Not at all - I released a customer support on Twitter dataset there specifically focused on unsupervised tasks! I think the focus on supervision in what people do with the data shows that there are still a lot of people poking around with the easier supervised tasks.
[0]: https://www.kaggle.com/thoughtvector/customer-support-on-twi...
Re: Datasets for Machine Learning
#20Surprised that CIFAR wasn’t mentioned under Images. I feel like that’s one of the standards, even more so than some of the ones that are listed.