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new 208a6fb [MINOR][DOC] Updated the documentation
208a6fb is described below
commit 208a6fb5e851b8307f31b87c600399ccd3c8552f
Author: Niketan Pansare <npan...@us.ibm.com>
AuthorDate: Thu Mar 21 09:51:42 2019 -0700
[MINOR][DOC] Updated the documentation
- Removed unnecessary external hyperlinks
---
index.md | 32 ++++++++++++++++----------------
1 file changed, 16 insertions(+), 16 deletions(-)
diff --git a/index.md b/index.md
index e7f16f3..4ceaee6 100644
--- a/index.md
+++ b/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
