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Re: Perl in data science: could a grant from Perl foundation be useful?

by thechartist (Monk)
on Feb 19, 2020 at 00:37 UTC ( #11113127=note: print w/replies, xml ) Need Help??


in reply to Perl in data science: could a grant from Perl foundation be useful?

There is nothing wrong with using Perl for data analysis if you know what you are trying to do. There are a number of options for conducting statistical analyses from a classical POV. Bayesian methods are sadly lacking right now, but you can always call out to R for that.

If you just want to unleash algorithms on vast quantities of data (of unverifiable quality), Perl has some "machine learning" options, but they are limited, as are tutorials.

A better use would be to see how machine learning algorithms could improve Perl on systems that do not get a lot of testing. That is what I am focusing on right now. I expect I'll need to write bindings to various C++ libraries, which is not the most appealing of options.

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Re^2: Perl in data science: could a grant from Perl foundation be useful?
by zubenel0 (Sexton) on Feb 20, 2020 at 18:40 UTC

    That seems pretty interesting what are you trying to do, could you elaborate a little more? How do you think machine learning could benefit in improving Perl? As a personal experience, sometimes I encounter troubles by using Perl on Windows and on Linux it works just fine...

      I am trying to formalize a model/process that can ID distributions where a patch on one is highly likely to also work on another. Retrospectively, we can patch multiple models and reduce time bug hunting. Prospectively, we can configure build scripts to eliminate certain problems.

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