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-rw-r--r-- | site/documentation.html | 6 | ||||
-rw-r--r-- | site/mllib/index.html | 8 |
2 files changed, 6 insertions, 8 deletions
diff --git a/site/documentation.html b/site/documentation.html index 04859ff6b..4741fe729 100644 --- a/site/documentation.html +++ b/site/documentation.html @@ -295,9 +295,11 @@ Slides, videos and EC2-based exercises from each of these are available online: </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> diff --git a/site/mllib/index.html b/site/mllib/index.html index 6216948f4..70165d0f7 100644 --- a/site/mllib/index.html +++ b/site/mllib/index.html @@ -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> |