Fix minor issues with api pages. (apache#6410)

1. In the notes section for ndarray, references did not seem clear enough to be referring to mxnet.ndarray or numpy.ndarray. Added the package names as prefixes to make it more obvious.
2. "share the same C++ operator source codes" => "share the same code". Since we don't really need to throw in more details than required.
3. Other relatively  minor language changes which will be obvious from the diff.

Note that I'm relatively not sure about the need for 1/ since it makes things more verbose. Let me know if it unnecessary and I'll remove it.

docs/api/python/index.md

@@ -1,7 +1,7 @@
 # MXNet - Python API
 
 MXNet provides a rich Python API to serve a broad community of Python developers.
-In this section, we provide a in-depth discussion of the functionality provided by
+In this section, we provide an in-depth discussion of the functionality provided by
 various MXNet Python packages. We have included code samples for most of the APIs
 for improved clarity. These code samples will run as-is as long as MXNet is first
 imported by running:

docs/api/python/ndarray.md

@@ -6,7 +6,7 @@
 
 ## Overview
 
-This document lists the routines of the *n*-dimensional array package
+This document lists the routines of the *n*-dimensional array package:
 
 ```eval_rst
 .. autosummary::
@@ -17,7 +17,7 @@ This document lists the routines of the *n*-dimensional array package
 
 The `NDArray` API, defined in the `ndarray` (or simply `nd`) package, provides
 imperative tensor operations on CPU/GPU.
-A `NDArray` represents a multidimensional, fixed-size homogenous array.
+An `NDArray` represents a multi-dimensional, fixed-size homogenous array.
 
 ```python
 >>> x = mx.nd.array([[1, 2, 3], [4, 5, 6]])
@@ -36,29 +36,31 @@ A `NDArray` represents a multidimensional, fixed-size homogenous array.
 
 A detailed tutorial is available at
 [NDArray - Imperative tensor operations on CPU/GPU](http://mxnet.io/tutorials/basic/ndarray.html).
+<br><br>
 
 ```eval_rst
 
-.. note:: ``mxnet.ndarray`` is similar to ``numpy.ndarray`` in some aspects. But the difference is not negligible. For example
+.. note:: ``mxnet.ndarray`` is similar to ``numpy.ndarray`` in some aspects. But the differences are not negligible. For instance:
 
-   - ``NDArray.T`` does real data transpose to return new a copied array, instead
-     of returning a view of the input array.
-   - ``ndarray.dot`` performs dot between the last axis of the first input array
-     and the first axis of the second input, while `numpy.dot` uses the second
-     last axis of the input array.
+   - ``mxnet.ndarray.NDArray.T`` does real data transpose to return new a copied 
+     array, instead of returning a view of the input array.
+   - ``mxnet.ndarray.dot`` performs dot product between the last axis of the
+     first input array and the first axis of the second input, while `numpy.dot`
+     uses the second last axis of the input array.
 
-   In additional, ``NDArray`` supports GPU computation and various neural
+   In addition, ``mxnet.ndarray.NDArray`` supports GPU computation and various neural
    network layers.
 
-.. note:: ``ndarray`` also provides almost same routines to ``symbol``. Most
-  routines between these two packages share the same C++ operator source
-  codes. But ``ndarray`` differs to ``symbol`` in several aspects:
+.. note:: ``ndarray`` provides almost the same routines as ``symbol``. Most
+  routines between these two packages share the source code. But ``ndarray``
+  differs from ``symbol`` in few aspects:
 
   - ``ndarray`` adopts imperative programming, namely sentences are executed
-    step-by-step so that the results can be obtained immediately.
+    step-by-step so that the results can be obtained immediately whereas 
+    ``symbol`` adopts declarative programming.
 
-  - Most binary operators such as ``+`` and ``>`` are enabled broadcasting in
-    default.
+  - Most binary operators in ``ndarray`` such as ``+`` and ``>`` have
+    broadcasting enabled by default.
 ```
 
 In the rest of this document, we first overview the methods provided by the

docs/api/python/symbol.md

@@ -40,19 +40,20 @@ array([ 4.,  7.], dtype=float32)
 ```
 
 A detailed tutorial is available at [Symbol - Neural network graphs and auto-differentiation](http://mxnet.io/tutorials/basic/symbol.html).
+<br><br>
 
 ```eval_rst
 
-.. note:: most operators provided in ``symbol`` are similar to ``ndarray``. But
-   also note that ``symbol`` differs to ``ndarray`` in several aspects:
+.. note:: most operators provided in ``symbol`` are similar to those in ``ndarray``
+   although there are few differences:
 
    - ``symbol`` adopts declarative programming. In other words, we need to first
-     composite the computations, and then feed with data to execute.
+     compose the computations, and then feed it with data for execution whereas
+     ndarray adopts imperative programming.
 
-   - Most binary operators such as ``+`` and ``>`` are not enabled broadcasting.
-     We need to call the broadcasted version such as ``broadcast_plus``
+   - Most binary operators in ``symbol`` such as ``+`` and ``>`` don't broadcast.
+     We need to call the broadcast version of the operator such as ``broadcast_plus``
      explicitly.
-
 ```
 
 In the rest of this document, we first overview the methods provided by the