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https://issues.apache.org/jira/browse/SPARK-5575?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14987405#comment-14987405
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Disha Shrivastava commented on SPARK-5575:
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Hi,
I am working on a project to implement data parallelism using downpour SGD
(http://research.google.com/archive/large_deep_networks_nips2012.html) in
Spark. I have modified the implementation present at
https://github.com/guoding83128/OpenDL . I wanted to know if someone is working
on an implementation on this or has done it already.
> Artificial neural networks for MLlib deep learning
> --------------------------------------------------
>
> Key: SPARK-5575
> URL: https://issues.apache.org/jira/browse/SPARK-5575
> Project: Spark
> Issue Type: Umbrella
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Alexander Ulanov
>
> Goal: Implement various types of artificial neural networks
> Motivation: deep learning trend
> Requirements:
> 1) Basic abstractions such as Neuron, Layer, Error, Regularization, Forward
> and Backpropagation etc. should be implemented as traits or interfaces, so
> they can be easily extended or reused
> 2) Implement complex abstractions, such as feed forward and recurrent networks
> 3) Implement multilayer perceptron (MLP), convolutional networks (LeNet),
> autoencoder (sparse and denoising), stacked autoencoder, restricted
> boltzmann machines (RBM), deep belief networks (DBN) etc.
> 4) Implement or reuse supporting constucts, such as classifiers, normalizers,
> poolers, etc.
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