This is an automated email from the ASF dual-hosted git repository. niketanpansare pushed a commit to branch gh-pages in repository https://gitbox.apache.org/repos/asf/systemml.git
The following commit(s) were added to refs/heads/gh-pages by this push: 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