[systemml] branch master updated: [MINOR][DOC] Updated the documentation

[ni...@apache.org]

This is an automated email from the ASF dual-hosted git repository.

niketanpansare pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/systemml.git


The following commit(s) were added to refs/heads/master by this push:
     new bc02283  [MINOR][DOC] Updated the documentation
bc02283 is described below

commit bc022839d489329c5e9bf1ca763c6596697110cb
Author: Niketan Pansare <np...@us.ibm.com>
AuthorDate: Thu Mar 21 09:51:42 2019 -0700

    [MINOR][DOC] Updated the documentation
    
    - Removed unnecessary external hyperlinks
---
 docs/index.md | 32 ++++++++++++++++----------------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/docs/index.md b/docs/index.md
index e7f16f3..4ceaee6 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -42,26 +42,26 @@ This version of SystemML supports: Java 8+, Scala 2.11+, Python 2.7/3.5+, Hadoop
 
 * If you are new to SystemML, please refer to the [installation guide](http://systemml.apache.org/install-systemml.html) and try out our [sample notebooks](http://systemml.apache.org/get-started.html#sample-notebook)
 * If you want to invoke one of our [pre-implemented algorithms](algorithms-reference):
-  * Using Python, consider using 
-    * the convenient [mllearn API](http://apache.github.io/systemml/python-reference.html#mllearn-api). The usage is describe in our [beginner's guide](http://apache.github.io/systemml/beginners-guide-python.html#invoke-systemmls-algorithms)  
-    * OR [Spark MLContext](spark-mlcontext-programming-guide) API
-  * Using Java/Scala, consider using 
+  * In Python, consider using 
+    * the convenient [mllearn API](http://apache.github.io/systemml/python-reference.html#mllearn-api). The usage is described in our [beginner's guide](http://apache.github.io/systemml/beginners-guide-python.html#invoke-systemmls-algorithms)  
+    * Or [Spark MLContext](spark-mlcontext-programming-guide) API
+  * In Java/Scala, consider using 
     * [Spark MLContext](spark-mlcontext-programming-guide) API for large datasets
-    * OR [JMLC](jmlc) API for in-memory scoring
+    * Or [JMLC](jmlc) API for in-memory scoring
   * Via Command-line, follow the usage section in the [Algorithms Reference](algorithms-reference) 
 * If you want to implement a deep neural network, consider
-  * specifying your network in [Keras](https://keras.io/) format and invoking it with our [Keras2DML](beginners-guide-keras2dml) API
-  * OR specifying your network in [Caffe](http://caffe.berkeleyvision.org/) format and invoking it with our [Caffe2DML](beginners-guide-caffe2dml) API
-  * OR Using DML-bodied [NN library](https://github.com/apache/systemml/tree/master/scripts/nn). The usage is described in our [sample notebook](https://github.com/apache/systemml/blob/master/samples/jupyter-notebooks/Deep%20Learning%20Image%20Classification.ipynb)
-* Since training a deep neural network is often compute-bound, you may want to
-  * Enable [native BLAS](native-backend) in SystemML
-  * OR run it [using our GPU backend](gpu)  
+  * Specifying your network in [Keras](https://keras.io/) format and invoking it with [Keras2DML](beginners-guide-keras2dml) API
+  * Or specifying your network in [Caffe](http://caffe.berkeleyvision.org/) format and invoking it with [Caffe2DML](beginners-guide-caffe2dml) API
+  * Or using DML-bodied [NN library](https://github.com/apache/systemml/tree/master/scripts/nn). The usage is described in our [sample notebook](https://github.com/apache/systemml/blob/master/samples/jupyter-notebooks/Deep%20Learning%20Image%20Classification.ipynb)
+* Since training a deep neural network is often compute-bound, you may want to enable SystemML's
+  * [native BLAS](native-backend)
+  * Or [GPU backend](gpu)
 * If you want to implement a custom machine learning algorithm and you are familiar with:
-  * [R](https://www.r-project.org/about.html), consider implementing your algorithm in [DML](dml-language-reference) (recommended)
-  * [Python](https://www.python.org/), you can implement your algorithm in [PyDML](beginners-guide-to-dml-and-pydml) or using the [matrix class](http://apache.github.io/systemml/python-reference.html#matrix-class)
-* If you want to try out SystemML on single machine (for example, your laptop), consider
-  * using the above mentioned APIs with [Apache Spark](https://spark.apache.org/downloads.html) (recommended). Please refer to our [installation guide](http://systemml.apache.org/install-systemml.html).
-  * OR running it using java in [standalone mode](standalone-guide)
+  * R syntax, consider implementing your algorithm in [DML](dml-language-reference) (recommended)
+  * Python syntax, you can implement your algorithm in [PyDML](beginners-guide-to-dml-and-pydml) or using the [matrix class](http://apache.github.io/systemml/python-reference.html#matrix-class)
+* If you want to try out SystemML on your laptop, consider
+  * using the above mentioned APIs with Apache Spark (recommended). Please refer to our [installation guide](http://systemml.apache.org/install-systemml.html) for instructions on how to setup SystemML on your laptop
+  * Or running SystemML in the [standalone mode](standalone-guide) with Java
 
 ## Running SystemML