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Posted to dev@mahout.apache.org by "Suneel Marthi (JIRA)" <ji...@apache.org> on 2014/02/25 17:01:20 UTC
[jira] [Commented] (MAHOUT-1426) GSOC 2013 Neural network
algorithms
[ https://issues.apache.org/jira/browse/MAHOUT-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13911680#comment-13911680 ]
Suneel Marthi commented on MAHOUT-1426:
---------------------------------------
The classifier.mlp is a supercised classifier based on Online learning training using SGD. There are old JIRAs that had RBM implementation (not MapReduce) - Mahout-968 and one for Autoencoders (MAhout-732). Both of which never made it to the codebase.
> GSOC 2013 Neural network algorithms
> -----------------------------------
>
> Key: MAHOUT-1426
> URL: https://issues.apache.org/jira/browse/MAHOUT-1426
> Project: Mahout
> Issue Type: Improvement
> Components: Classification
> Reporter: Maciej Mazur
>
> I would like to ask about possibilites of implementing neural network algorithms in mahout during GSOC.
> There is a classifier.mlp package with neural network.
> I can't see neighter RBM nor Autoencoder in these classes.
> There is only one word about Autoencoders in NeuralNetwork class.
> As far as I know Mahout doesn't support convolutional networks.
> Is it a good idea to implement one of these algorithms?
> Is it a reasonable amount of work?
> How hard is it to get GSOC in Mahout?
> Did anyone succeed last year?
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Re: [jira] [Commented] (MAHOUT-1426) GSOC 2013 Neural network algorithms
Posted by Ted Dunning <te...@gmail.com>.
Doing a non-map-reduce neural network in Mahout would be of substantial
interest.
I don't see a role for something that is 10x slower than it should be.
On Tue, Feb 25, 2014 at 10:03 AM, Maciej Mazur <ma...@gmail.com>wrote:
> I understand that neural networks aren't perfectly suitable for MapReduce.
> But if there is very large network and lagre training set it seems to be a
> good solution to use MapReduce.
>
> RBMs and Autoencoders are used for pretraining. It allows to learn better
> representation for deep architectures (acording to
> http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf). Deep
> supervised
> multi-layer Neural Networks are very hard to train, starting from random
> initialization.
>
>
>
> On Tue, Feb 25, 2014 at 5:01 PM, Suneel Marthi (JIRA) <jira@apache.org
> >wrote:
>
> >
> > [
> >
> https://issues.apache.org/jira/browse/MAHOUT-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13911680#comment-13911680
> ]
> >
> > Suneel Marthi commented on MAHOUT-1426:
> > ---------------------------------------
> >
> > The classifier.mlp is a supercised classifier based on Online learning
> > training using SGD. There are old JIRAs that had RBM implementation (not
> > MapReduce) - Mahout-968 and one for Autoencoders (MAhout-732). Both of
> > which never made it to the codebase.
> >
> > > GSOC 2013 Neural network algorithms
> > > -----------------------------------
> > >
> > > Key: MAHOUT-1426
> > > URL: https://issues.apache.org/jira/browse/MAHOUT-1426
> > > Project: Mahout
> > > Issue Type: Improvement
> > > Components: Classification
> > > Reporter: Maciej Mazur
> > >
> > > I would like to ask about possibilites of implementing neural network
> > algorithms in mahout during GSOC.
> > > There is a classifier.mlp package with neural network.
> > > I can't see neighter RBM nor Autoencoder in these classes.
> > > There is only one word about Autoencoders in NeuralNetwork class.
> > > As far as I know Mahout doesn't support convolutional networks.
> > > Is it a good idea to implement one of these algorithms?
> > > Is it a reasonable amount of work?
> > > How hard is it to get GSOC in Mahout?
> > > Did anyone succeed last year?
> >
> >
> >
> > --
> > This message was sent by Atlassian JIRA
> > (v6.1.5#6160)
> >
>
Re: [jira] [Commented] (MAHOUT-1426) GSOC 2013 Neural network algorithms
Posted by Maciej Mazur <ma...@gmail.com>.
I understand that neural networks aren't perfectly suitable for MapReduce.
But if there is very large network and lagre training set it seems to be a
good solution to use MapReduce.
RBMs and Autoencoders are used for pretraining. It allows to learn better
representation for deep architectures (acording to
http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf). Deep supervised
multi-layer Neural Networks are very hard to train, starting from random
initialization.
On Tue, Feb 25, 2014 at 5:01 PM, Suneel Marthi (JIRA) <ji...@apache.org>wrote:
>
> [
> https://issues.apache.org/jira/browse/MAHOUT-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13911680#comment-13911680]
>
> Suneel Marthi commented on MAHOUT-1426:
> ---------------------------------------
>
> The classifier.mlp is a supercised classifier based on Online learning
> training using SGD. There are old JIRAs that had RBM implementation (not
> MapReduce) - Mahout-968 and one for Autoencoders (MAhout-732). Both of
> which never made it to the codebase.
>
> > GSOC 2013 Neural network algorithms
> > -----------------------------------
> >
> > Key: MAHOUT-1426
> > URL: https://issues.apache.org/jira/browse/MAHOUT-1426
> > Project: Mahout
> > Issue Type: Improvement
> > Components: Classification
> > Reporter: Maciej Mazur
> >
> > I would like to ask about possibilites of implementing neural network
> algorithms in mahout during GSOC.
> > There is a classifier.mlp package with neural network.
> > I can't see neighter RBM nor Autoencoder in these classes.
> > There is only one word about Autoencoders in NeuralNetwork class.
> > As far as I know Mahout doesn't support convolutional networks.
> > Is it a good idea to implement one of these algorithms?
> > Is it a reasonable amount of work?
> > How hard is it to get GSOC in Mahout?
> > Did anyone succeed last year?
>
>
>
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> This message was sent by Atlassian JIRA
> (v6.1.5#6160)
>