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From "Tommaso Teofili (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HAMA-681) Multi Layer Perceptron
Date Wed, 15 May 2013 10:05:18 GMT

    [ https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13658209#comment-13658209
] 

Tommaso Teofili commented on HAMA-681:
--------------------------------------

Hi Sascha, all looking progress here sounds really nice! 

[~twist], you could even try to leverage Hama implementation of Gradient Descent, see
- http://svn.apache.org/repos/asf/hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/GradientDescentBSP.java
- http://svn.apache.org/repos/asf/hama/trunk/examples/src/main/java/org/apache/hama/examples/GradientDescentExample.java
                
> Multi Layer Perceptron 
> -----------------------
>
>                 Key: HAMA-681
>                 URL: https://issues.apache.org/jira/browse/HAMA-681
>             Project: Hama
>          Issue Type: New Feature
>          Components: machine learning
>            Reporter: Christian Herta
>
> Implementation of a Multilayer Perceptron (Neural Network)
>  - Learning by Backpropagation 
>  - Distributed Learning
> The implementation should be the basis for the long range goals:
>  - more efficent learning (Adagrad, L-BFGS)
>  - High efficient distributed Learning
>  - Autoencoder - Sparse (denoising) Autoencoder
>  - Deep Learning
>  
> ---
> Due to the overhead of Map-Reduce(MR) MR didn't seem to be the best strategy to distribute
the learning of MLPs.
> Therefore the current implementation of the MLP (see MAHOUT-976) should be migrated to
Hama. First all dependencies to Mahout (Matrix-Library) must be removed to get a standalone
MLP Implementation. Then the Hama BSP programming model should be used to realize distributed
learning.
> Different strategies of efficient synchronized weight updates has to be evaluated.
> Resources:
>  Videos:
>     - http://www.youtube.com/watch?v=ZmNOAtZIgIk
>     - http://techtalks.tv/talks/57639/
>  MLP and Deep Learning Tutorial:
>  - http://www.stanford.edu/class/cs294a/
>  Scientific Papers:
>  - Google's "Brain" project: 
> http://research.google.com/archive/large_deep_networks_nips2012.html
>  - Neural Networks and BSP: http://ipdps.cc.gatech.edu/1998/biosp3/bispp4.pdf
>  - http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf

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