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t3kcit

Re:Python vs Julia – an example from machine learning on Wednesday March 12, 2014 @05:21AM (#7384667)
Why are you not using typed memory views in Cython? That might easily close the performance gap.
Re:Machine Learning Cheat Sheet (for scikit-learn) on Sunday January 27, 2013 @05:35PM (#5125966)
I did a another post on that one. http://peekaboo-vision.blogspot.de/2013/01/pystruct-more-str... The post still needs some more detail, though.
Re:Machine Learning Cheat Sheet (for scikit-learn) on Sunday January 27, 2013 @07:13AM (#5123997)
Thanks :) We should really add online naive Bayes and it would be really easy. But no-one got around to it yet :-/ Feel free ;)
Re:Machine Learning Cheat Sheet (for scikit-learn) on Sunday January 27, 2013 @05:52AM (#5123874)
Or you could use pystruct: https://github.com/amueller/pystruct But I saved that for another blog post ;)

That is BSD licensed and pure Python, has some more feature than SVMstruct but is not as optimized (yet).

Re:Why use SVM on Wednesday December 26, 2012 @08:52AM (#4969197)
Re:Why use SVM on Wednesday December 26, 2012 @08:52AM (#4969196)
I'm a bit disappointed that such an unreflective post makes it to hacker news. Many of the scikit-learn staff have excellent blogs, I never saw any of those featured.

Btw, the sklearn docs have an example that I think explains different classifiers way better and also hints at a very important point: non-linear decision boundaries are mostly important for low-dimensional dense data and not helpful for text data for example.

Bad title. Should be "In Germany, police has to inform public how and when they monitor online communications."

(Apart from that this is not really news, I could have told you month ago if you asked me ;)

Re:Super-resolution from a single image on Saturday July 14, 2012 @07:49PM (#4245661)
In the context of computer vision, this paper is pretty "old". Since then people have more focused on denoising, though.