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.
[1] http://haskell.org/haskellwiki/HNN
On Tue, Mar 30, 2010 at 1:55 PM, Grzegorz C
Hi,
Ketil Malde-5 wrote:
Once upon a time, I proposed a GSoC project for a machine learning library.
I still get some email from prospective students about this, whom I discourage as best I can by saying I don't have the time or interest to pursue it, and that chances aren't so great since you guys tend to prefer language-related stuff instead of application-related stuff.
But if anybody disagrees with my sentiments and is willing to mentor this, there are some smart students looking for an opportunity. I'd be happy to forward any requests.
I don't know whether this is a good idea for a GSoC project, but I would certainly welcome such a library. I am using Haskell a bit for statistical NLP: in my experience currently Haskell is excellent for the components which deal with data preprocessing and feature extraction, but when it comes to implementing the core training algorithms and running them on large data sets, it's easy to get very poor performance and/or unexpected stack overflows. So if a library could provide some well-tuned and tested building blocks for implementing the performance critical parts of machine learning algorithms, it would improve the coding experience in a major way.
Best, -- Grzegorz
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