Hello Marcus,
Thank you for your reply!
That sounds interesting, can you tell us more about the project you worked
> on?
There is a planetarium program called KStars that is a part of KDE
Education package. This program allows you see the map of the night sky,
explore different space objects, control your telescope and a lot of other
amateur astronomy-related stuff. My task was to make an Android version
called KStars Lite that would share the same codebase (though I migrated
the whole graphics part to the new graphical backend, the data part is
still the same). You can check my work on this page (nickname: polaris)
https://summerofcode.withgoogle.com/archive/2016/projects/5053062041305088/
and try it yourself on your Android device
https://play.google.com/store/apps/details?id=org.kde.kstars.lite.
I was working with Qt framework that is based on C++ so I have some C++
background.
There is definitely room to extend/improve the existing collaborative
> filtering
> framework, there might even be the option to combine deep learning with
> collaborative filtering: https://github.com/robi56/Deep-Learning-for-
> Recommendation-Systems
That sounds very interesting! I will definitely look at the papers and read
the ones I like.
We are trying to add new entrance issues over the next days,
That would be very nice, thank you very much.
Regards, Artem
On Mon, Feb 5, 2018 at 7:48 PM, Marcus Edel
wrote:
> Hello Artem,
>
> thanks for getting in touch.
>
> My name is Artem Fedoskin. I have already written to this mailing list but
> I
> think it would be good to introduce myself. I study a Master's Degree in
> Data
> Analytics at University of Hildesheim (Germany). I have already
> participated in
> GSoC 2016 in KDE with the project that involved C++ and Qt framework
> (KStars
> Lite).
>
>
> That sounds interesting, can you tell us more about the project you worked
> on?
>
> Is it possible that more than one student will work on Deep Learning
> Modules? We
> could implement different algorithms.
>
>
> Yes, that's possible, note that the models on the ideas page are just
> suggestions, if you like to implement another interesting model please
> feel free
> to start a discussion.
>
> Apart from Deep Learning I'm very interested in collaborative filtering.
> Our
> lectures have already covered some of the main concepts and I'm very
> interested
> in that area.
>
>
> There is definitely room to extend/improve the existing collaborative
> filtering
> framework, there might even be the option to combine deep learning with
> collaborative filtering: https://github.com/robi56/Deep-Learning-for-
> Recommendation-Systems
>
> I have compiled mlpack and tried several examples. I'm currently looking
> at what
> I could do on GitHub but I would be very grateful if you could point me to
> some
> issue related to the projects I'm interested in.
>
>
> We are trying to add new entrance issues over the next days, in the
> meantime,
> you can always glance over the codebase and perhaps think about ways to
> improve
> or extend a specific method.
>
> I hope some I said was helpful, let us know if we should clarify anything.
>
> Thanks,
> Marcus
>
> On 5. Feb 2018, at 02:41, Artem Fedoskin wrote:
>
> Dear developers of mlpack,
>
> My name is Artem Fedoskin. I have already written to this mailing list but
> I think it would be good to introduce myself. I study a Master's Degree in
> Data Analytics at University of Hildesheim (Germany). I have already
> participated in GSoC 2016 in KDE with the project that involved C++ and Qt
> framework (KStars Lite).
>
> Indeed I'm very interested in Machine Learning and I was very happy to
> find your library. Though now I primarily use Python for Data Science
> purposes, I would be very happy to use my C++ knowledge for Machine
> Learning.
>
> I'm particularly interested in following projects:
> 1. Essential Deep Learning Modules
> 2. Alternatives to neighborhood-based collaborative filtering
> 3. Reinforcement Learning
>
> Is it possible that more than one student will work on Deep Learning
> Modules? We could implement different algorithms.
>
> Apart from Deep Learning I'm very interested in collaborative filtering.
> Our lectures have already covered some of the main concepts and I'm very
> interested in that area.
>
> I have compiled mlpack and tried several examples. I'm currently looking
> at what I could do on GitHub but I would be very grateful if you could
> point me to some issue related to the projects I'm interested in.
>
> Regards, Artem Fedoskin
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>
>
>
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