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From Domino Valdano <dvald...@pivotal.io>
Subject [ANNOUNCE] Apache MADlib 1.16 released
Date Tue, 09 Jul 2019 19:39:09 GMT
The Apache MADlib team is pleased to announce the immediate
availability of MADlib version 1.16.

Highlights of the MADlib 1.16 release:

New features:
    - Deep learning: support for Keras with TensorFlow backend with GPU
    - acceleration
    - Deep learning: utility to load model architectures and weights
    - Deep learning: preprocess images for gradient descent optimization
      algorithms
    - kd-tree method for k-nearest neighbors for faster approximate
      solution
    - Support for Greenplum 6
    - Support for PostgreSQL 11

Bug fixes, including:
    - Jaccard distance was not releasing memory
    - MLP with minibatching on postgres fixed
    - MLP was not stopping after tolerance was reached
    - MLP warm start fixed
    - MLP with minibatch for integer dependent variable on PostgreSQL
    - Pivot: array_agg/distinct scaling issue on gpdb fixed
    - linregr_train with dependent variable a JSONB element fixed
    - MADLib 1.15 was not recognizing Postgres 10 declarative partitioned
      table
    - Encoding module with bigint fixed
    - SVM class_weight param fixed

Other:
    - Simplified maintenance by removing online examples from sql functions
    - Improved performance for weakly connected components
    - SVD and other messaging improvements
    - max itemset size default in assoc rules changed to 10

The complete release notes can be found here:

  https://cwiki.apache.org/confluence/display/MADLIB/MADlib+1.16

You can download the source release and convenience binary packages
from Apache MADlib's download page here:

  http://madlib.apache.org/download.html

Alternatively, you can download through an ASF mirror near you:

  https://www.apache.org/dyn/closer.lua/madlib/1.16

----

Apache MADlib is an open-source library for scalable in-database
analytics. It provides data-parallel implementations of mathematical,
statistical and machine learning methods for structured and
unstructured data.

The MADlib mission: to foster widespread development of scalable
analytic skills, by harnessing efforts from commercial practice,
academic research, and open-source development.

We welcome your help and feedback. For more information on how to
report problems, and to get involved, visit the project website at
https://madlib.apache.org

----

Thank you, everyone who contributed to the MADlib 1.16 release. We
look forward to continued community participation for the next
release.

Warm Regards,
Domino Valdano

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