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https://issues.apache.org/jira/browse/SPARK-5575?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14988705#comment-14988705
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Alexander Ulanov commented on SPARK-5575:
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Hi Disha,
RNN is a major feature. I suggest to start from a smaller contribution. Spark
contains the implementation of multi-layer perceptron since version 1.5. New
features are supposed to re-use its code and follow the internal API that it
has introduced.
> 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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