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From "Edward J. Yoon" <edwardy...@apache.org>
Subject Re: Multilayer Perceptron
Date Thu, 15 Nov 2012 12:19:55 GMT
This is for you :-)


On Thu, Nov 15, 2012 at 6:09 PM, info@christianherta.de
<info@christianherta.de> wrote:
> Dear All,
> what do you think about to scale out the learning of Multi Layer Perceptrons
> (MLP) with BSPs?
> I heard the talk of Tommaso at the apacheConAt first glance the pramming model
> BSP seems to fit better the MapReduce for this purpose.
> The basic idea is to distribute the backprop algorithm is the following:
> Distribution of learning can be done by (batch learning):
> 1 Partioning of the data in x chunks
> 2 On each working node: Learning the weight changes (as matrices) in each chunk
> 3 Combining the matrixes (weight changes) and simultaneous update of the weights
> in each node - back to 2
> Maybe this procedure can be done with random parts of the chunks (distributed
> quasi online learning).
> I wrote the (basic) backprob algorithm of a multi layer preceptron (see mahout
> patch https://issues.apache.org/jira/browse/MAHOUT-976). It uses the Mahout
> Matrix Library, which is under the hood the Colt Matrix Library from Cern.
> Probably using the Cern Matrix Library would also suitable for Hama. Then it
> could be easy to port the MLP to Hama.
> What do you think about it?
> Thanks for you response.
> Cheers
>  Christian

Best Regards, Edward J. Yoon

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