[GitHub] eric-haibin-lin commented on a change in pull request #7921: Add three sparse tutorials

[gi...@git.apache.org]

eric-haibin-lin commented on a change in pull request #7921: Add three sparse tutorials
URL: https://github.com/apache/incubator-mxnet/pull/7921#discussion_r140672266
 
 

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 File path: docs/tutorials/sparse/train.md
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+
+# Train a Linear Regression Model with Sparse Symbols
+In previous tutorials, we introduced `CSRNDArray` and `RowSparseNDArray`,
+the basic data structures for manipulating sparse data.
+MXNet also provides `Sparse Symbol` API, which enables symbolic expressions that handle sparse arrays.
+In this tutorial, we first focus on how to compose a symbolic graph with sparse operators,
+then train a linear regression model using sparse symbols with the Module API.
+
+## Prerequisites
+
+To complete this tutorial, we need:
+
+- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html).  
+
+- [Jupyter Notebook](http://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages.
+```
+pip install jupyter requests
+```
+
+- Basic knowledge of Symbol in MXNet. See the detailed tutorial for Symbol in [Symbol - Neural network graphs and auto-differentiation](https://mxnet.incubator.apache.org/tutorials/basic/symbol.html).
+
+- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [TODO(haibin) Add Link Here](http://ec2-54-187-32-207.us-west-2.compute.amazonaws.com/tutorials/sparse/csr.html).
+
+- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [TODO(haibin) Add Link Here](http://ec2-54-187-32-207.us-west-2.compute.amazonaws.com/tutorials/sparse/rowsparse.html).
+
+## Variables
+
+Variables are placeholder for arrays. We can use them to hold sparse arrays, too.
+
+### Variable Storage Types
+
+The `stype` attribute of a variable is used to indicate the storage type of the array.
+By default, the `stype` of a variable is "default" which indicates the default dense storage format.
+We can specify the `stype` of a variable as "csr" or "row_sparse" to hold sparse arrays.
+
+
+```python
+import mxnet as mx
+# Create a variable to hold an NDArray
+a = mx.sym.Variable('a')
+# Create a variable to hold a CSRNDArray
+b = mx.sym.Variable('b', stype='csr')
+# Create a variable to hold a RowSparseNDArray
+c = mx.sym.Variable('c', stype='row_sparse')
+(a, b, c)
+```
+
+### Bind with Sparse Arrays
+
+The sparse symbols constructed above declare storage types of the arrays to hold.
+To evaluate them, we need to feed the free variables with sparse data.
+
+You can instantiate an executor from a sparse symbol by using the `simple_bind` method,
+which allocate zeros to all free variables according to their storage types.
+The executor provides `forward` method for evaluation and an attribute
 
 Review comment:
   which sentence are you referring to? bind will allocate the memory, forward will perform evaluation
 
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