Author: pat
Date: Sun Apr 12 17:12:25 2015
New Revision: 1673031
URL: http://svn.apache.org/r1673031
Log:
added some links and more explanation
Modified:
mahout/site/mahout_cms/trunk/content/index.mdtext
Modified: mahout/site/mahout_cms/trunk/content/index.mdtext
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/index.mdtext?rev=1673031&r1=1673030&r2=1673031&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/index.mdtext (original)
+++ mahout/site/mahout_cms/trunk/content/index.mdtext Sun Apr 12 17:12:25 2015
@@ -28,9 +28,20 @@
</div>
<p>The three major components of Mahout are an environment for building
scalable algorithms, many new Scala + Spark (H2O in progress) algorithms, and
Mahout's mature Hadoop MapReduce algorithms.</p>
<h4>**11 Apr 2015 - Apache Mahout's next generation version 0.10.0
released**</h4>
- <p>**Apache Mahout introduces a new math** [**environment we call
Samsara**](http://mahout.apache.org/users/sparkbindings/home.html), for its
theme of universal renewal. It reflects a fundamental rethinking of how
scalable machine learning algorithms are built and customized. Mahout-Samsara
is here to help people create their own math while providing some off-the-shelf
algorithm implementations. At its base are general linear algebra and
statistical operations along with the data structures to support them. Itâs
written in Scala with Mahout-specific extensions, and runs most fully on
Spark.</p>
-
- <p>[**Mahout
Algorithms**](http://mahout.apache.org/users/basics/algorithms.html) include
many new implementations built for speed on Mahout-Samsara. They run on Spark
and some on H2o, which means as much as a 10x speed increase. Youâll find
robust matrix decomposition algorithms as well as a Naive Bayes classifier and
collaborative filtering.</p>
+ <p>**Apache Mahout introduces a new math environment we call**
[**Samsara**](http://mahout.apache.org/users/sparkbindings/home.html),
+ for its theme of universal renewal. It reflects a fundamental rethinking
of how scalable machine learning
+ algorithms are built and customized. Mahout-Samsara is here to help people
create their own math while providing
+ some off-the-shelf algorithm implementations. At its base are general
linear algebra and statistical operations
+ along with the data structures to support them. Itâs written in Scala
with Mahout-specific extensions that look something like R,
+ and runs most fully on Spark. Mahout-Samsara comes with an interactive
shell that runs distributed operations on a Spark cluster.
+ This make prototyping or task submission much easier than before and allows
users to customize algorithms with
+ a whole ne degree of freedom.</p>
+
+ <p>[**Mahout
Algorithms**](http://mahout.apache.org/users/basics/algorithms.html) include
many new
+ implementations built for speed on Mahout-Samsara. They run on Spark and
some on H2O, which means as
+ much as a 10x speed increase. Youâll find robust matrix decomposition
algorithms as well as a **[Naive Bayes][1]**
+ classifier and collaborative filtering. The new spark-itemsimilarity
enables the next generation of **[cooccurrence
+ recommenders][2]** that can use entire user click streams and context in
making recommendations.</p>
<p>Interested in helping? Join the <a
href="https://mahout.apache.org/general/mailing-lists,-irc-and-archives.html";>Mailing
lists</a>.</p>
@@ -60,3 +71,7 @@ Visit our [release notes](http://mahout.
The book Mahout in Action is available in print. Sean Owen, Robin Anil, Ted
Dunning and Ellen Friedman thank the community (especially those who were
reviewers) for input during the process and hope it is enjoyable.
Find it at your favorite bookstore, or [order print and eBook copies from
Manning](http://manning.com/owen/) -- use discount code "mahout37" for 37% off.
+
+
+ [1]: http://mahout.apache.org/users/algorithms/spark-naive-bayes.html
+ [2]: http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html
\ No newline at end of file
