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https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13672828#comment-13672828
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Edward J. Yoon commented on HAMA-681:
-------------------------------------
Thanks, Yexi. I'll look at tonight when I get home.
{quote}
Note:
This version of MLP is the basic version, I will add more features if this one
passes the review.
BTW, I have checked out hama from Apache svn repository. Since I don't have
permission, I cannot create any branch.
{quote}
You have to follow the 'HowToContribute' development process[1] until you
become a full fledge committer. Of course, if you have different idea about
coding conventions, Please feel free to open the discuss or vote thread on dev@
list. This will help you learn something interesting or exciting. :)
1. https://wiki.apache.org/hama/HowToContribute
> 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
> Assignee: Yexi Jiang
> Labels: patch, perceptron
> Attachments: HAMA-681.patch, perception.patch
>
>
> 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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