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https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Christian Herta updated MAHOUT-976:
-----------------------------------
Description:
Implement a multi layer perceptron
* via Matrix Multiplication
* Learning by Backpropagation; implementing tricks by Yann LeCun et al.:
"Efficent Backprop"
* arbitrary number of hidden layers (also 0 - just the linear model)
* connection between proximate layers only
* different cost and activation functions (different activation function in
each layer)
* test of backprop by gradient checking
First:
* implementation "stocastic gradient descent" like gradient machine
* simple gradient descent incl. momentum
Later (new jira issues):
* Distributed Batch learning (see below)
* "Stacked (Denoising) Autoencoder" - Feature Learning
* advanced cost minimazation like 2nd order methods, conjugate gradient etc.
Distribution of learning can be done by (batch learning):
1 Partioning of the data in x chunks
2 Learning the weight changes as matrices in each chunk
3 Combining the matrixes and update of the weights - back to 2
Maybe this procedure can be done with random parts of the chunks (distributed
quasi online learning).
Batch learning with delta-bar-delta heuristics for adapting the learning rates.
was:
Implement a multi layer perceptron
* via Matrix Multiplication
* Learning by Backpropagation; implementing tricks by Yann LeCun et al.:
"Efficent Backprop"
* arbitrary number of hidden layers (also 0 - just the linear model)
* connection between proximate layers only
* different cost and activation functions (different activation function in
each layer)
* test of backprop by gradient checking
First:
* implementation "stocastic gradient descent" like gradient machine
* simple gradient descent
Later (new jira issues):
* momentum for better and faster learning
* advanced cost minimazation like 2nd order methods, conjugate gradient etc.
* Distributed Batch learning (see below)
* "Stacked (Denoising) Autoencoder" - Feature Learning
Distribution of learning can be done by (batch learning):
1 Partioning of the data in x chunks
2 Learning the weight changes as matrices in each chunk
3 Combining the matrixes and update of the weights - back to 2
Maybe this procedure can be done with random parts of the chunks (distributed
quasi online learning)
> Implement Multilayer Perceptron
> -------------------------------
>
> Key: MAHOUT-976
> URL: https://issues.apache.org/jira/browse/MAHOUT-976
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.7
> Reporter: Christian Herta
> Priority: Minor
> Labels: multilayer, networks, neural, perceptron
> Original Estimate: 80h
> Remaining Estimate: 80h
>
> Implement a multi layer perceptron
> * via Matrix Multiplication
> * Learning by Backpropagation; implementing tricks by Yann LeCun et al.:
> "Efficent Backprop"
> * arbitrary number of hidden layers (also 0 - just the linear model)
> * connection between proximate layers only
> * different cost and activation functions (different activation function in
> each layer)
> * test of backprop by gradient checking
>
> First:
> * implementation "stocastic gradient descent" like gradient machine
> * simple gradient descent incl. momentum
> Later (new jira issues):
> * Distributed Batch learning (see below)
> * "Stacked (Denoising) Autoencoder" - Feature Learning
> * advanced cost minimazation like 2nd order methods, conjugate gradient etc.
> Distribution of learning can be done by (batch learning):
> 1 Partioning of the data in x chunks
> 2 Learning the weight changes as matrices in each chunk
> 3 Combining the matrixes and update of the weights - back to 2
> Maybe this procedure can be done with random parts of the chunks (distributed
> quasi online learning).
> Batch learning with delta-bar-delta heuristics for adapting the learning
> rates.
>
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