Update homepage (dmlc#75)

* Update homepage

* cos sim

docs/index.rst

@@ -12,9 +12,10 @@ models. This toolkit offers four main features:
 4. Community supports
 
 
-This toolkit assume users has basic knowledges about deep learning and
-NLP. Otherwise, please refer to introduction course such as `Stanford
-CS224n <http://web.stanford.edu/class/cs224n/>`_.
+This toolkit assumes that users have basic knowledge about deep learning and
+NLP. Otherwise, please refer to an introduction course such as 
+`Deep Learning---The Straight Dope <http://gluon.mxnet.io/>`_ or
+`Stanford CS224n <http://web.stanford.edu/class/cs224n/>`_.
 
 .. note::
 
@@ -26,19 +27,19 @@ Installation
 
 GluonNLP relies on the recent version of MXNet. The easiest way to install MXNet
 is through `pip <https://pip.pypa.io/en/stable/installing/>`_. The following
-command installs a nightly build CPU version of MXNet.
+command installs a nightly built CPU version of MXNet.
 
 .. code-block:: bash
 
    pip install --pre mxnet
 
 .. note::
 
-   There are other pre-build MXNet packages that enables GPU supports and
+   There are other pre-build MXNet packages that enable GPU supports and
    accelerate CPU performance, please refer to `this tutorial
    <http://gluon-crash-course.mxnet.io/mxnet_packages.html>`_ for details. Some
    training scripts are recommended to run on GPUs, if you don't have a GPU
-   machine at hands, you may consider to `run on AWS
+   machine at hands, you may consider `running on AWS
    <http://gluon-crash-course.mxnet.io/use_aws.html>`_.
 
 
@@ -52,8 +53,8 @@ Then install the GluonNLP toolkit by
 A Quick Example
 ----------------
 
-Here is a quick example that download and create a word embedding model and then
-compare the similarity between two words.
+Here is a quick example that downloads and creates a word embedding model and then
+computes the cosine similarity between two words.
 
 ..
    (You can click the go button on the
@@ -76,10 +77,13 @@ compare the similarity between two words.
    import mxnet as mx
    import gluonnlp as nlp
 
-   glove = nlp.embedding.create('glove', source='glove.6B.50d.txt')
+   # Create a GloVe word embedding.
+   glove = nlp.embedding.create('glove', source='glove.6B.50d')
+   # Obtain vectors for 'baby' and 'infant' in the GloVe word embedding. 
    baby, infant = glove['baby'], glove['infant']
 
    def cos_similarity(vec1, vec2):
+       # Normalize the dot product of two vectors with the L2-norm product.
        return mx.nd.dot(vec1, vec2) / (vec1.norm() * vec2.norm())
 
    print(cos_similarity(baby, infant))