Note that, if any student is interested, the Haskell Neural Network library [1] is being rewritten from scratch. We (Thomas Bereknyi and I) are discussing many core data structure alternatives, with some suggestions from Edward Kmett. There may even be some room for a rewrite or update of fgl, possibly with an alternative conception, to fit well HNN. I am definitely not sure if this is worth a GSoC and if the community would benefit that much from such a work, but it's there.<div>
<br></div><div>[1] <a href="http://haskell.org/haskellwiki/HNN">http://haskell.org/haskellwiki/HNN</a><br><br><div class="gmail_quote">On Tue, Mar 30, 2010 at 1:55 PM, Grzegorz C <span dir="ltr"><<a href="mailto:pitekus@gmail.com">pitekus@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><br>
Hi,<br>
<div class="im"><br>
<br>
Ketil Malde-5 wrote:<br>
><br>
> Once upon a time, I proposed a GSoC project for a machine learning<br>
> library.<br>
><br>
> I still get some email from prospective students about this, whom I<br>
> discourage as best I can by saying I don't have the time or interest to<br>
> pursue it, and that chances aren't so great since you guys tend to<br>
> prefer language-related stuff instead of application-related stuff.<br>
><br>
> But if anybody disagrees with my sentiments and is willing to mentor<br>
> this, there are some smart students looking for an opportunity. I'd be<br>
> happy to forward any requests.<br>
><br>
<br>
</div>I don't know whether this is a good idea for a GSoC project, but I would<br>
certainly welcome such a library. I am using Haskell a bit for statistical<br>
NLP: in my experience currently Haskell is excellent for the components<br>
which deal with data preprocessing and feature extraction, but when it comes<br>
to implementing the core training algorithms and running them on large data<br>
sets, it's easy to get very poor performance and/or unexpected stack<br>
overflows. So if a library could provide some well-tuned and tested building<br>
blocks for implementing the performance critical parts of machine learning<br>
algorithms, it would improve the coding experience in a major way.<br>
<br>
Best,<br>
--<br>
Grzegorz<br>
<font color="#888888"><br>
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</font><div class="im">Sent from the Haskell - Haskell-Cafe mailing list archive at Nabble.com.<br>
<br>
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