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) <[email protected]>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) >
