svn commit: r1673031 - /mahout/site/mahout_cms/trunk/content/index.mdtext

[pa...@apache.org]

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. It’s 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. You’ll 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. It’s 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. You’ll 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
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