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Posted to commits@spark.apache.org by pw...@apache.org on 2015/04/29 03:39:27 UTC
svn commit: r1676653 - in /spark: documentation.md site/documentation.html
site/mllib/index.html
Author: pwendell
Date: Wed Apr 29 01:39:27 2015
New Revision: 1676653
URL: http://svn.apache.org/r1676653
Log:
Updating book listing
Modified:
spark/documentation.md
spark/site/documentation.html
spark/site/mllib/index.html
Modified: spark/documentation.md
URL: http://svn.apache.org/viewvc/spark/documentation.md?rev=1676653&r1=1676652&r2=1676653&view=diff
==============================================================================
--- spark/documentation.md (original)
+++ spark/documentation.md Wed Apr 29 01:39:27 2015
@@ -137,9 +137,11 @@ Slides, videos and EC2-based exercises f
</ul>
<h3>Books</h3>
-
<ul>
- <li><a href="http://shop.oreilly.com/product/0636920028512.do">Learning Spark</a>, by Holden Karau, Andy Konwinski, Patrick Wendell and Matei Zaharia (early release ebook from O'Reilly Media)</li>
+ <li><a href="http://shop.oreilly.com/product/0636920028512.do">Learning Spark</a>, by Holden Karau, Andy Konwinski, Patrick Wendell and Matei Zaharia (O'Reilly Media)</li>
+ <li><a href="http://www.manning.com/bonaci/">Spark in Action</a>, by Marko Bonaci and Petar Zecevic (Manning)</li>
+ <li><a href="http://shop.oreilly.com/product/0636920035091.do">Advanced Analytics with Spark</a>, by Sandy Ryza, Uri Laserson, Sean Owen and Josh Wills (O'Reilly Media)</li>
+ <li><a href="http://www.manning.com/bonaci/">Spark GraphX in Action</a>, by Michael Malak (Manning)</li>
<li><a href="http://www.packtpub.com/fast-data-processing-with-spark/book">Fast Data Processing with Spark</a>, by Holden Karau (Packt Publishing)</li>
</ul>
Modified: spark/site/documentation.html
URL: http://svn.apache.org/viewvc/spark/site/documentation.html?rev=1676653&r1=1676652&r2=1676653&view=diff
==============================================================================
--- spark/site/documentation.html (original)
+++ spark/site/documentation.html Wed Apr 29 01:39:27 2015
@@ -295,9 +295,11 @@ Slides, videos and EC2-based exercises f
</ul>
<h3>Books</h3>
-
<ul>
- <li><a href="http://shop.oreilly.com/product/0636920028512.do">Learning Spark</a>, by Holden Karau, Andy Konwinski, Patrick Wendell and Matei Zaharia (early release ebook from O'Reilly Media)</li>
+ <li><a href="http://shop.oreilly.com/product/0636920028512.do">Learning Spark</a>, by Holden Karau, Andy Konwinski, Patrick Wendell and Matei Zaharia (O'Reilly Media)</li>
+ <li><a href="http://www.manning.com/bonaci/">Spark in Action</a>, by Marko Bonaci and Petar Zecevic (Manning)</li>
+ <li><a href="http://shop.oreilly.com/product/0636920035091.do">Advanced Analytics with Spark</a>, by Sandy Ryza, Uri Laserson, Sean Owen and Josh Wills (O'Reilly Media)</li>
+ <li><a href="http://www.manning.com/bonaci/">Spark GraphX in Action</a>, by Michael Malak (Manning)</li>
<li><a href="http://www.packtpub.com/fast-data-processing-with-spark/book">Fast Data Processing with Spark</a>, by Holden Karau (Packt Publishing)</li>
</ul>
Modified: spark/site/mllib/index.html
URL: http://svn.apache.org/viewvc/spark/site/mllib/index.html?rev=1676653&r1=1676652&r2=1676653&view=diff
==============================================================================
--- spark/site/mllib/index.html (original)
+++ spark/site/mllib/index.html Wed Apr 29 01:39:27 2015
@@ -252,20 +252,16 @@
<div class="col-md-4 col-padded">
<h3>Algorithms</h3>
<p>
- MLlib 1.3 contains the following algorithms:
+ MLlib 1.1 contains the following algorithms:
</p>
<ul class="list-narrow">
<li>linear SVM and logistic regression</li>
<li>classification and regression tree</li>
- <li>random forest and gradient-boosted trees</li>
- <li>clusteirng via k-means, Gaussian mixtures, and power iteration clustering</li>
- <li>topic modeling via latent Dirichlet allocation</li>
+ <li>k-means clustering</li>
<li>recommendation via alternating least squares</li>
<li>singular value decomposition</li>
<li>linear regression with L<sub>1</sub>- and L<sub>2</sub>-regularization</li>
- <li>isotonic regression</li>
<li>multinomial naive Bayes</li>
- <li>frequent itemset mining via FP-growth</li>
<li>basic statistics</li>
<li>feature transformations</li>
</ul>
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