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docs & tutorial without letting them go out of date, I think that's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
<jmschreibe...@gmail.com <mailto: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 <mailto: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,
--
Makina Corpus <http://makina-corpus.com>
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