[gnuastro-devel] [task #15080] Machine learning in Gnuastro

[Mohammad Akhlaghi]

URL:
  <https://savannah.gnu.org/task/?15080>

                 Summary: Machine learning in Gnuastro
                 Project: GNU Astronomy Utilities
            Submitted by: makhlaghi
            Submitted on: Tue 30 Oct 2018 02:20:50 PM CET
         Should Start On: Tue 30 Oct 2018 12:00:00 AM CET
   Should be Finished on: Tue 30 Oct 2018 12:00:00 AM CET
                Category: Libraries
                Priority: 5 - Normal
              Item Group: None
                  Status: In Progress
                 Privacy: Public
        Percent Complete: 0%
             Assigned to: None
             Open/Closed: Open
         Discussion Lock: Any
                  Effort: 0.00

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Details:

Machine learning libraries are increasingly being used in astronomy, or
scientific applications in general. While it does have many caveats that must
not be forgotten (see P.S.), it does also have benefits which can be useful.
For example see arXiv:1810.10098 <https://arxiv.org/abs/1810.10098> in task
#14980.

I am starting this task for a general review of possible libraries (or
generally, existing infra-structure) that we may be able to use in Gnuastro to
enable Machine learning applications within Gnuastro's programs and libraries.
Here the best summary list of libraries in various languages that I have found
so far:

https://github.com/josephmisiti/awesome-machine-learning

We don't have to only look at those written in C. We can also link with
libraries originally written in other languages, if they have a C interface.
For example Torch <http://torch.ch> is written in Lua, but they claim has an
"amazing interface to C, via LuaJIT".

P.S. For a nice general discussion of Machine learning caveats see (link to
YouTube) this talk <https://www.youtube.com/watch?v=Qi1Yry33TQE> by Ali Rahimi
upon receiving the "Test-of-time" award at NIPS (Neural Information Processing
Systems) 2017 conference. In this talk he compares Machine learning to
alchemy. A (Facebook link) rebuttable
<https://www.facebook.com/yann.lecun/posts/10154938130592143> was also
published by Yann LeCun afterwards.




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