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https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13672705#comment-13672705
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Edward J. Yoon commented on HAMA-681:
-------------------------------------
1) Every source files should include the Apache license header. Please see
http://www.apache.org/legal/src-headers.html
2) Please use code formatter. In a multi-developer project, team collaboration
is essential for the success of the project. Consistent code formatting
improves readability and enhance teamwork. Here's a eclipse settings file that
you can import - http://hama.apache.org/files/hama-eclipse-formatter.xml
To use:
- Open up Window > Preferences and navigate to the Java > Code Style >
Formatter page
- Press the import button and select the hama-eclipse-formatter.xml file
- Then, you can apply the formatter to a selected area of a Java source code
files by pressing 'Shift+Ctrl+F'.
Please check out the Hama trunk (SVN) from ASF repository and use SVN if you're
going to continue to make some changes.
> 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: 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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