Making spelling and general grammar corrections (apache#4771)

docs/README.md

@@ -1,11 +1,11 @@
 # MXNet documentation
 
-A built version of document is available at http://mxnet.dmlc.ml
+A built version of document is available at http://mxnet.io
 
-## To build docs with Docker
+## To build the docs with Docker
 
 The `Dockerfile` in this directory encapsulates all the dependencies needed
-to build the docs.  The default entrypoint builds the docs and serves them
+to build the docs.  The default entry-point builds the docs and serves them
 through a simple HTTP server for previewing.
 
 ```
@@ -23,11 +23,11 @@ docker image each time, you can do this with
 docker run -it -p 8008:8008 -v `pwd`:/opt/mxnet/docs mxnet/docs
 ```
 
-which maps your current directory into the docker image to get any local 
+which maps your current directory into the docker image to get any local
 changes.
 
 **NOTE:** Any changes to the API reference will not get rebuilt this way.
-The API reference docs are introspected from the built binaries, which 
+The API reference docs are introspected from the built binaries, which
 in this Dockerfile are pulled from github/dmlc/master.  To work-around
 this, map a volume with your code changes into the container, and rebuild
 MXNet in the container before doing the doc build.  Or use the local

docs/_static/mxnet-theme/navbar.html

@@ -42,7 +42,7 @@
 Previous Navbar Layout End -->
 
 <div class="navbar navbar-fixed-top">
-<a href="https://github.com/dmlc/mxnet"><img style="position: absolute; top: 0; right: 0; border: 0; z-index: 0" 
+<a href="https://github.com/dmlc/mxnet"><img style="position: absolute; top: 0; right: 0; border: 0; z-index: 0"
 src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/github_fork_me_green_ribbon.png" alt="Fork me on GitHub"/>
   <div class="container" id="navContainer">
     <div id="header-inner" class="innder">
@@ -65,14 +65,14 @@ <h1 id="logo-wrap">
             <li><a class="main-nav-link" href="{{url_root}}api/python/io.html">- I/O</a></li>
             <li><a class="main-nav-link" href="{{url_root}}api/python/ndarray.html">- NDArray</a></li>
             <li><a class="main-nav-link" href="{{url_root}}api/python/kvstore.html">- KVStore</a></li>
+            <li><a class="main-nav-link" href="{{url_root}}api/scala/index.html">Scala</a></li>
             <li><a class="main-nav-link" href="{{url_root}}api/r/index.html">R</a></li>
             <li><a class="main-nav-link" href="{{url_root}}api/julia/index.html">Julia</a></li>
             <li><a class="main-nav-link" href="{{url_root}}api/c++/index.html">C++</a></li>
-            <li><a class="main-nav-link" href="{{url_root}}api/scala/index.html">Scala</a></li>
           </ul>
           {% endfor %}
         </span>
-		
+
         <a class="main-nav-link" href="{{url_root}}architecture/index.html">Architecture</a>
 		<a class="main-nav-link" href="{{url_root}}community/index.html">Community</a>
       </nav>
@@ -87,7 +87,7 @@ <h1 id="logo-wrap">
               <li class="dropdown-submenu">
                 <a href="#" tabindex="-1">{{name}}</a>
                 <ul class="dropdown-menu">
-                  {% for lang in ['Python', 'R', 'Julia', 'C++', 'Scala'] %}
+                  {% for lang in ['Python', 'Scala', 'R', 'Julia', 'C++'] %}
                     <li><a tabindex="-1" href="{{url_root}}{{name.lower()|replace(" ", "_")}}/{{lang.lower()}}/index.html">{{lang}}</a>
                     </li>
                   {% endfor %}

docs/api/julia/index.md

@@ -1,10 +1,10 @@
 MXNet - Julia API
 =================
-MXNet supports Julia programming language. The MXNet Julia package brings flexible and efficient GPU
-computing and state-of-art deep learning to Julia.
+MXNet supports the Julia programming language. The MXNet Julia package brings flexible and efficient GPU
+computing and the state-of-art deep learning to Julia.
 
 - It enables you to write seamless tensor/matrix computation with multiple GPUs in Julia.
-- It also enables you construct and customize the state-of-art deep learning models in Julia,
+- It also enables you to construct and customize the state-of-art deep learning models in Julia,
   and apply them to tasks such as image classification and data science challenges.
 
 

docs/api/python/index.md

@@ -1,13 +1,13 @@
-# Python APIs for MXNet
+# MXNet - Python API
 
 MXNet supports the Python programming language. The MXNet Python package brings flexible and efficient GPU
 computing and state-of-art deep learning to Python. It enables you to write seamless tensor/matrix computation with multiple GPUs in Python. It also allows you to construct and customize state-of-art deep learning models in Python,
   and apply them to tasks, such as image classification and data science challenges.
 
 
 ## Python API Reference
-* [Module API](module.md) is a flexible high-level interface for training neural networks.
-* [Model API](model.md) is an alternate simple high-level interface for training neural networks.
+* [Module API](module.md) is a flexible high-level interface for training the neural networks.
+* [Model API](model.md) is an alternate simple high-level interface for training the neural networks.
 * [Symbolic API](symbol.md) performs operations on NDArrays to assemble neural networks from layers.
 * [IO Data Loading API](io.md) performs parsing and data loading.
 * [NDArray API](ndarray.md) performs vector/matrix/tensor operations.

docs/api/python/io.md

@@ -5,7 +5,7 @@ MXNet provides basic iterators for MNIST and Recordio images. To hide the cost o
 
 Topics:
 
-* [Data Iterator Parameters](#parameters-for-data-iterator) clarifies the different usages for dataiter parameters.
+* [Data Iterator Parameters](#parameters-for-data-iterator) clarifies the different usages for Dataiter parameters.
 * [Create a Data Iterator](#create-a-data-iterator) introduces how to create a data iterator in MXNet for Python.
 * [How to Get Data](#how-to-get-data) introduces the data resource and data preparation tools.
 * [IO API Reference](#io-api-reference) explains the IO API.
@@ -137,7 +137,7 @@ For more details, run ```./bin/im2rec```.
 The `im2rec` tool and `mx.io.ImageRecordIter` have multi-label support for a single image.
 For example, if you have four labels for a single image, you can use the following procedure to use the Recordio tools.
 
-1. Write the the image list files as follows:
+1. Write the image list files as follows:
 
      ```
          integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image

docs/api/python/model.md

@@ -1,8 +1,8 @@
-# MXNet Python Model API
+# Model API
 
 The model API provides a simplified way to train neural networks using common best practices.
 It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
-modules that makes neural network training easy.
+modules that make neural network training easy.
 
 Topics:
 
@@ -16,7 +16,7 @@ Topics:
 
 ## Train the Model
 
-To train a model, perform two steps: configure the model  using the symbol parameter,
+To train a model, perform two steps: configure the model using the symbol parameter,
 then call ```model.Feedforward.create``` to create the model.
 The following example creates a two-layer neural network.
 
@@ -62,7 +62,7 @@ We also provide save and load functions.
     # load model back
     model_loaded = mx.model.FeedForward.load(prefix, iteration)
 ```
-The advantage of these save and load functions is that they are language agnostic.
+The advantage of these save and load functions are that they are language agnostic.
 You should be able to save and load directly into cloud storage, such as Amazon S3 and HDFS.
 
 ##  Periodic Checkpointing

docs/api/python/module.md

@@ -1,10 +1,10 @@
-# Module Interface 
-The module API provides an intermediate- and high-level interface for performing computation with neural networks in MXNet. A *module* is an instance of subclasses of `BaseModule`. The most widely used module class is simply called `Module`, which wraps a `Symbol` and one or more `Executors`. For a full list of functions, see  `BaseModule`. 
+# Module API
+The module API provides an intermediate and high-level interface for performing computation with neural networks in MXNet. A *module* is an instance of subclasses of the `BaseModule`. The most widely used module class is simply called `Module`, which wraps a `Symbol` and one or more `Executors`. For a full list of functions, see  `BaseModule`.
 Each subclass of modules might have some extra interface functions. In this topic, we provide some examples of common use cases. All of the module APIs are in the `mxnet.module` namespace, simply called `mxnet.mod`.
 
 ## Preparing a Module for Computation
 
-To construct a module, refer to the constructors for the specific module class. For example, the `Module` class takes a `Symbol` as input:
+To construct a module, refer to the constructors for the specific module class. For example, the `Module` class accepts a `Symbol` as the input:
 
 ```python
     import mxnet as mx
@@ -17,7 +17,7 @@ To construct a module, refer to the constructors for the specific module class.
     act2 = mx.symbol.Activation(fc2, name='relu2', act_type="relu")
     fc3  = mx.symbol.FullyConnected(act2, name='fc3', num_hidden=10)
     out  = mx.symbol.SoftmaxOutput(fc3, name = 'softmax')
- 
+
     # construct the module
     mod = mx.mod.Module(out)
 ```
@@ -51,7 +51,7 @@ The interface is very similar to the old `FeedForward` class. You can pass in ba
     mod.predict(val_dataiter)
 ```
 
-The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the `predict()` function. 
+The module collects and returns all of the prediction results. For more details about the format of the return values, see the documentation for the `predict()` function.
 
 When prediction results might be too large to fit in memory, use the `iter_predict` API:
 

docs/api/python/symbol.md

@@ -1,4 +1,4 @@
-# MXNet Python Symbolic API
+# Symbol API
 
 Topics:
 

docs/api/python/symbol_in_pictures.md

@@ -11,11 +11,11 @@ graph that describes the computation. The following picture shows how you compos
 ![Symbol Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_basic.png)
 
 - The ```mxnet.symbol.Variable``` function creates argument nodes that represent input to the computation.
-- The symbol is overloaded with basic element-wise mathematical operations. 
+- The symbol is overloaded with basic element-wise mathematical operations.
 
 ## Configure Neural Networks
 
-In addition to supporting fine-grained operations, MXNet  provides a way to perform big operations that is analogous to layers in neural networks.
+In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
 You can use operators to describe the configuration of a neural network.
 
 ![Net Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_net.png)
@@ -28,10 +28,10 @@ The following example shows how to configure multiple input neural networks.
 ![Multi Input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_multi_in.png)
 
 
-## Bind and Execute Symbol 
+## Bind and Execute Symbol
 
 When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
-in order to obtain an ```Executor```.
+in order to obtain a ```Executor```.
 
 ![Bind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/bind_basic.png)
 
@@ -42,8 +42,7 @@ To get the output results, given the bound NDArrays as input, you can call ```Ex
 
 ## Bind Multiple Outputs
 
-To group symbols, then bind them to 
-get outputs of both, use ```mx.symbol.Group```.
+To group symbols, then bind them to get outputs of both, use ```mx.symbol.Group```.
 
 ![MultiOut](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_multi_out.png)
 
@@ -68,8 +67,7 @@ the Executor for you.
 
 ## Auxiliary States
 
-Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states  might 
-not be part of the computation, they can be helpful to track. You can pass auxiliary states in the same way that you pass arguments.
+Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful to track. You can pass auxiliary states in the same way that you pass arguments.
 
 ![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_aux_state.png)
 

docs/api/scala/io.md

@@ -137,7 +137,7 @@ For more details, run ```./bin/im2rec```.
 The `im2rec` tool and `IO.ImageRecordIter` have multi-label support for a single image.
 For example, if you have four labels for a single image, you can use the following procedure to use the Recordio tools.
 
-1. Write the the image list files as follows:
+1. Write the image list files as follows:
 
      ```
          integer_image_index \t label_1 \t label_2 \t   label_3 \t label_4 \t path_to_image

docs/api/scala/model.md

@@ -2,7 +2,7 @@
 
 The model API provides a simplified way to train neural networks using common best practices.
 It's a thin wrapper built on top of the [ndarray](ndarray.md) and [symbolic](symbol.md)
-modules that makes neural network training easy.
+modules that make neural network training easy.
 
 Topics:
 
@@ -14,7 +14,7 @@ Topics:
 
 ## Train the Model
 
-To train a model, perform two steps: configure the model  using the symbol parameter,
+To train a model, perform two steps: configure the model using the symbol parameter,
 then call ```model.Feedforward.create``` to create the model.
 The following example creates a two-layer neural network.
 

docs/api/scala/symbol.md

@@ -58,7 +58,7 @@ For proper communication with the C++ back end, both the key and values of the a
     res6: Option[String] = Some(angry)
 ```
 
-To attach attributes, we can use ```AttrScope```. ```AttrScope``` automatically adds the specified attributes to all of the symbols created within that scope. User can also inherit this object to change naming behavior. For example:
+To attach attributes, we can use ```AttrScope```. ```AttrScope``` automatically adds the specified attributes to all of the symbols created within that scope. The user can also inherit this object to change naming behavior. For example:
 
 ```scala
     val (data, gdata) =

docs/api/scala/symbol_in_pictures.md

@@ -11,11 +11,11 @@ graph that describes the computation. The following picture shows how you compos
 ![Symbol Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_basic.png)
 
 - The ```mxnet.symbol.Variable``` function creates argument nodes that represent input to the computation.
-- The symbol is overloaded with basic element-wise mathematical operations. 
+- The symbol is overloaded with basic element-wise mathematical operations.
 
 ## Configure Neural Networks
 
-In addition to supporting fine-grained operations, MXNet  provides a way to perform big operations that is analogous to layers in neural networks.
+In addition to supporting fine-grained operations, MXNet provides a way to perform big operations that are analogous to layers in neural networks.
 You can use operators to describe the configuration of a neural network.
 
 ![Net Compose](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_net.png)
@@ -28,10 +28,10 @@ The following example shows how to configure multiple input neural networks.
 ![Multi Input](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/compose_multi_in.png)
 
 
-## Bind and Execute Symbol 
+## Bind and Execute Symbol
 
 When you need to execute a symbol graph, you call the bind function to bind ```NDArrays``` to the argument nodes
-in order to obtain an ```Executor```.
+in order to obtain a ```Executor```.
 
 ![Bind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/bind_basic.png)
 
@@ -42,8 +42,7 @@ To get the output results, given the bound NDArrays as input, you can call ```Ex
 
 ## Bind Multiple Outputs
 
-To group symbols, then bind them to 
-get outputs of both, use ```mx.symbol.Group```.
+To group symbols, then bind them to get outputs of both, use ```mx.symbol.Group```.
 
 ![MultiOut](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_multi_out.png)
 
@@ -68,8 +67,7 @@ the Executor for you.
 
 ## Auxiliary States
 
-Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states  might 
-not be part of the computation, they can be helpful to track. You can pass auxiliary states in the same way that you pass arguments.
+Auxiliary states are just like arguments, except that you can't take the gradient of them. Although auxiliary states might not be part of the computation, they can be helpful to track. You can pass auxiliary states in the same way that you pass arguments.
 
 ![SimpleBind](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/symbol/executor_aux_state.png)
 

docs/architecture/index.md

@@ -29,7 +29,3 @@ In this section, we will describe MXNet System architecture and various componen
 * [How to read the code](http://mxnet.io/architecture/read_code.html)
 * [Develop and hack MXNet](http://mxnet.io/how_to/develop_and_hack.html)
 * [How to contribute to MXNet](http://mxnet.io/community/contribute.html)
-
-
-
-