Fix a few bugs in the release notes.

2 files changed, 7 insertions, 7 deletions

diff --git a/releases/_posts/2016-07-26-spark-release-2-0-0.md b/releases/_posts/2016-07-26-spark-release-2-0-0.md
index 1cc5cddd9..ab19aa389 100644
--- a/releases/_posts/2016-07-26-spark-release-2-0-0.md
+++ b/releases/_posts/2016-07-26-spark-release-2-0-0.md
@@ -73,16 +73,16 @@ In addition, when building without Hive support, Spark SQL should have almost al
 
 
 ### MLlib
-The DataFrame-based API is now the primary API. The RDD-based API is entering maintenance mode. See the MLlib guide for details
+The DataFrame-based API is now the primary API. The RDD-based API is entering maintenance mode. See the [MLlib guide](http://spark.apache.org/docs/2.0.0/ml-guide.html) for details
 
 ####  New features
 
-- ML persistence: The DataFrames-based API provides near-complete support for saving and loading ML models and Pipelines in Scala, Java, Python, and R.  See this blog post for details.  (SPARK-6725, SPARK-11939, SPARK-14311)
-- MLlib in R: SparkR now offers MLlib APIs for generalized linear models, naive Bayes, k-means clustering, and survival regression.  See this talk to learn more.
+- ML persistence: The DataFrames-based API provides near-complete support for saving and loading ML models and Pipelines in Scala, Java, Python, and R.  See this [blog post](https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html) and the following JIRAs for details: SPARK-6725, SPARK-11939, SPARK-14311.
+- MLlib in R: SparkR now offers MLlib APIs for generalized linear models, naive Bayes, k-means clustering, and survival regression.  See [this talk](https://spark-summit.org/2016/events/recent-developments-in-sparkr-for-advanced-analytics/) to learn more.
 - Python: PySpark now offers many more MLlib algorithms, including LDA, Gaussian Mixture Model, Generalized Linear Regression, and more.
 - Algorithms added to DataFrames-based API: Bisecting K-Means clustering, Gaussian Mixture Model, MaxAbsScaler feature transformer.
 
-This talk lists many of these new features.
+[This talk](https://spark-summit.org/2016/events/apache-spark-mllib-20-preview-data-science-and-production/) lists many of these new features.
 
 #### Speed/scaling
 Vectors and Matrices stored in DataFrames now use much more efficient serialization, reducing overhead in calling MLlib algorithms. (SPARK-14850)
diff --git a/site/releases/spark-release-2-0-0.html b/site/releases/spark-release-2-0-0.html
index 66ca23bf1..83f949a21 100644
--- a/site/releases/spark-release-2-0-0.html
+++ b/site/releases/spark-release-2-0-0.html
@@ -282,13 +282,13 @@
 <h4 id="new-features-1">New features</h4>
 
 <ul>
-  <li>ML persistence: The DataFrames-based API provides near-complete support for saving and loading ML models and Pipelines in Scala, Java, Python, and R.  See this blog post for details.  (SPARK-6725, SPARK-11939, SPARK-14311)</li>
-  <li>MLlib in R: SparkR now offers MLlib APIs for generalized linear models, naive Bayes, k-means clustering, and survival regression.  See this talk to learn more.</li>
+  <li>ML persistence: The DataFrames-based API provides near-complete support for saving and loading ML models and Pipelines in Scala, Java, Python, and R.  See this <a href="https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html">blog post</a> and the following JIRAs for details: SPARK-6725, SPARK-11939, SPARK-14311.</li>
+  <li>MLlib in R: SparkR now offers MLlib APIs for generalized linear models, naive Bayes, k-means clustering, and survival regression.  See <a href="https://spark-summit.org/2016/events/recent-developments-in-sparkr-for-advanced-analytics/">this talk</a> to learn more.</li>
   <li>Python: PySpark now offers many more MLlib algorithms, including LDA, Gaussian Mixture Model, Generalized Linear Regression, and more.</li>
   <li>Algorithms added to DataFrames-based API: Bisecting K-Means clustering, Gaussian Mixture Model, MaxAbsScaler feature transformer.</li>
 </ul>
 
-<p>This talk lists many of these new features.</p>
+<p><a href="https://spark-summit.org/2016/events/apache-spark-mllib-20-preview-data-science-and-production/">This talk</a> lists many of these new features.</p>
 
 <h4 id="speedscaling">Speed/scaling</h4>
 <p>Vectors and Matrices stored in DataFrames now use much more efficient serialization, reducing overhead in calling MLlib algorithms. (SPARK-14850)</p>