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https://issues.apache.org/jira/browse/FLINK-1979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15286460#comment-15286460
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ASF GitHub Bot commented on FLINK-1979:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1985#discussion_r63509381
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/optimization/GradientDescent.scala
---
@@ -272,7 +272,7 @@ abstract class GradientDescent extends IterativeSolver {
* The regularization function is `1/2 ||w||_2^2` with `w` being the
weight vector.
*/
class GradientDescentL2 extends GradientDescent {
-
+ //TODO(skavulya): Pass regularization penalty as a parameter
--- End diff --
Is this TODO still valid? If so, can we resolve it?
> 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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