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Posted to dev@hama.apache.org by "Christian Herta (JIRA)" <ji...@apache.org> on 2012/11/23 17:14:58 UTC
[jira] [Created] (HAMA-681) Multi Layer Perceptron
Christian Herta created HAMA-681:
------------------------------------
Summary: Multi Layer Perceptron
Key: HAMA-681
URL: https://issues.apache.org/jira/browse/HAMA-681
Project: Hama
Issue Type: New Feature
Components: machine learning
Affects Versions: 0.5.0
Reporter: Christian Herta
Implementation of a Multilayer Perceptron (Neural Network)
- Learning by Backpropagation
- Distributed Learning
Implementation should be the basis for the long range goals:
- High efficient distributed Learning
- Autoencoder - Sparse (denoising) Autoencoder
- Deep Learning
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[jira] [Updated] (HAMA-681) Multi Layer Perceptron
Posted by "Christian Herta (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Christian Herta updated HAMA-681:
---------------------------------
Description:
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.
was:
Implementation of a Multilayer Perceptron (Neural Network)
- Learning by Backpropagation
- Distributed Learning
Implementation should be the basis for the long range goals:
- High efficient distributed Learning
- Autoencoder - Sparse (denoising) Autoencoder
- Deep Learning
> Multi Layer Perceptron
> -----------------------
>
> Key: HAMA-681
> URL: https://issues.apache.org/jira/browse/HAMA-681
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Affects Versions: 0.5.0
> 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.
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[jira] [Updated] (HAMA-681) Multi Layer Perceptron
Posted by "Christian Herta (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Christian Herta updated HAMA-681:
---------------------------------
Description:
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
was:
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/
- 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://www.stanford.edu/class/cs294a/
- http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
> Multi Layer Perceptron
> -----------------------
>
> Key: HAMA-681
> URL: https://issues.apache.org/jira/browse/HAMA-681
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Affects Versions: 0.5.0
> 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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[jira] [Updated] (HAMA-681) Multi Layer Perceptron
Posted by "Christian Herta (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Christian Herta updated HAMA-681:
---------------------------------
Description:
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/
- 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://www.stanford.edu/class/cs294a/
- http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
was:
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:
- 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://www.stanford.edu/class/cs294a/
- http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
> Multi Layer Perceptron
> -----------------------
>
> Key: HAMA-681
> URL: https://issues.apache.org/jira/browse/HAMA-681
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Affects Versions: 0.5.0
> 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/
> - 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://www.stanford.edu/class/cs294a/
> - http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
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[jira] [Updated] (HAMA-681) Multi Layer Perceptron
Posted by "Christian Herta (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Christian Herta updated HAMA-681:
---------------------------------
Description:
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:
- 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
was:
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.
> Multi Layer Perceptron
> -----------------------
>
> Key: HAMA-681
> URL: https://issues.apache.org/jira/browse/HAMA-681
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Affects Versions: 0.5.0
> 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:
> - 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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If you think it was sent incorrectly, please contact your JIRA administrators
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[jira] [Updated] (HAMA-681) Multi Layer Perceptron
Posted by "Christian Herta (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Christian Herta updated HAMA-681:
---------------------------------
Description:
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:
- 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://www.stanford.edu/class/cs294a/
- http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
was:
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:
- 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
> Multi Layer Perceptron
> -----------------------
>
> Key: HAMA-681
> URL: https://issues.apache.org/jira/browse/HAMA-681
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Affects Versions: 0.5.0
> 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:
> - 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://www.stanford.edu/class/cs294a/
> - http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
--
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If you think it was sent incorrectly, please contact your JIRA administrators
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