[scikit-learn] Documentation proposal

Andreas Mueller t3kcit at gmail.com
Fri Jun 16 17:39:48 EDT 2017

I'm pretty sure there's a new scikit-learn blog-post about every day,
with highly varying quality. I don't think it's a good idea to spend our 
time reviewing them.

On 06/14/2017 04:43 PM, Vlad Niculae wrote:
> 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 docs & tutorial without letting them go out of date, I think that's 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 
> <jmschreiber91 at gmail.com <mailto:jmschreiber91 at 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 at python.org <mailto:scikit-learn at 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,
>         -- 
>         Makina Corpus <http://makina-corpus.com>
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