Indeed, thank you, Gael! My 2c, not thought through very thoroughly, is that although a "related tutorials" would be great, it would be considerably more of a maintenance burden than scikit-learn-contrib, because docs go staler faster than code. We *could* force all code in the doc to be runnable and unit-tested, but that is probably not sufficient, because checking the text cannot really be done automatically. It would be great if we could figure out a system to enable community maintenance of related docs & tutorial without letting them go out of date, I think that's something we can think about.
Yours, Vlad On Wed, Jun 14, 2017 at 6:04 PM, Jacob Schreiber <jmschreibe...@gmail.com> wrote: > Hi Gael > > Thanks for the work! We are grateful for the work that other people do in > providing these types of tutorials and introductions as they lower the > barrier of entry for new people to get into machine learning. We generally > don't include these in the official sklearn documentation, in no small part > because it would be a time sink to decide from which among a large group of > tutorials should be included. That being said, perhaps we should consider > having a 'related tutorials' page similar to the 'related work' page, > serving as an aggregation of links? > > Jacob > > On Mon, Jun 12, 2017 at 12:17 PM, Gaël Pegliasco via scikit-learn < > scikit-learn@python.org> wrote: > >> Hi, >> >> First of all, thanks to all contributors for developping a such rich, >> simple, well documented and easy to use machine learning library for Python >> ; which, clearly, plays a big role in Python world domination in AI ! >> >> As I'm using it more and more these past month, I've written a french >> tutorial on machine learning introduction: >> >> - The Theory (no code here, only describing AI with Python and >> machine learning concepts with real examples): >> https://makina-corpus.com/blog/metier/2017/initiation-au- >> machine-learning-avec-python-theorie >> >> <https://makina-corpus.com/blog/metier/2017/initiation-au-machine-learning-avec-python-theorie> >> - The Practice (using Scikit-Learn) >> https://makina-corpus.com/blog/metier/2017/initiation-au- >> machine-learning-avec-python-pratique >> >> <https://makina-corpus.com/blog/metier/2017/initiation-au-machine-learning-avec-python-pratique> >> Another iris tutorial, but with much more details than most I've read >> using this database and using both supervised and unsupervised learning >> >> I've received a few positive returns regarding these 2 articles and >> others requests to translate it into english. >> >> I think that as to translate it into english, you may find it useful to >> include it into Scikit-Learn official documentation/examples ? >> >> So, if you think it can be useful I could work on it as soon as next week. >> >> Anyway, any feedback is welcome, especially because I'm not an expert >> and that it may not be error safe! >> >> Thanks again for your great work and keep going on ! >> >> Gaël, >> -- >> [image: Makina Corpus] <http://makina-corpus.com> >> Newsletters <http://makina-corpus.com/formulaires/newsletters> | >> Formations <http://makina-corpus.com/formation> | Twitter >> <https://twitter.com/makina_corpus> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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