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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1979) Implement Loss Functions
Date Mon, 11 May 2015 11:51:00 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14537855#comment-14537855

ASF GitHub Bot commented on FLINK-1979:

Github user thvasilo commented on the pull request:

    Thank you Johaness. The optimization code has been merged to the master now, so could
you rebase your branch to the latest master so we can look at the changes in an isolated way?
    You can take a look at the [How to contibute](http://flink.apache.org/how-to-contribute.html#contributing-code-&-documentation)
guide on how to do this. The merges you have currently make it hard to review the code.
    Also, please make sure all your classes have docstrings, you can take the docstring for
SquaredLoss as an example (i.e. one sentence is usually enough).
    Documentation is always welcome of course, so if you want to add some more details to
the loss functions section of the ML documentation (docs/libs/ml/optimization.md) feel free
to do so in this PR.
    Let me know if you run into any problems.

> Implement Loss Functions
> ------------------------
>                 Key: FLINK-1979
>                 URL: https://issues.apache.org/jira/browse/FLINK-1979
>             Project: Flink
>          Issue Type: Improvement
>          Components: Machine Learning Library
>            Reporter: Johannes Günther
>            Assignee: Johannes Günther
>            Priority: Minor
>              Labels: ML
> For convex optimization problems, optimizer methods like SGD rely on a pluggable implementation
of a loss function and its first derivative.

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