<div dir="ltr"><div>Hi Alessandro,</div><div><br></div><div>The advantage of StarCluster is the ability to run things in parallel. In the case of a Neural Net work you can have better/faster cross validation by training a different Neural nets on different nodes.</div>
<div><br></div><div>I know that there are Python Neural net libraries in the Python Scientific distributions. If you choose an AMI with a scientific Python distribution installed then this simplifies installations. I am note sure about AMIs with R, yet I am using an Anaconda AMI which has many scientific python libraries installed in it.</div>
<div><br></div><div>I hope you find this of help.</div><div><br></div><div> Jacob</div><div><br></div><div><br></div><div><br></div><div><br></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">
On Wed, Dec 11, 2013 at 1:59 PM, Alessandro Gagliardi <span dir="ltr"><<a href="mailto:alessandro.gagliardi@glassdoor.com" target="_blank">alessandro.gagliardi@glassdoor.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<div>I would like to train a neural net (or similar classifier) to predict one probabilistic value from 9 principal components. When I do it in R (using nnet) it caps at a few hundred observations, but that seems too small a sample when I have over 40k cases.
I know that the space of machine learning algorithms (distributed and otherwise) is vast and so was wondering if there was something that the StarCluster community might recommend. (Ideally it would be something that I could set up on StarCluster with minimal
difficulty.)</div>
<div><br>
</div>
<div>Thanks in advance,</div>
<div><a name="142e33e84a26e8a1__MailAutoSig" style="font-size:15px">-Alessandro Gagliardi</a></div>
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