[incubator-mxnet] branch master updated: Update index.md (#8760)

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The following commit(s) were added to refs/heads/master by this push:
     new ddb4782  Update index.md (#8760)
ddb4782 is described below

commit ddb47822fb2682ffba6c1e9466be301785f4be99
Author: Eric Junyuan Xie <pi...@users.noreply.github.com>
AuthorDate: Sat Dec 2 12:12:53 2017 -0800

    Update index.md (#8760)
    
    * Update index.md
    
    * Update index.md
    
    * Update index.md
---
 docs/tutorials/index.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 6429dfb..6432615 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -1,14 +1,11 @@
 # Tutorials
 
-These tutorials introduce a few fundamental concepts in deep learning and how to implement them in _MXNet_. The _Basics_ section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The _Training and Inference_ section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier.
-
-
 ## Gluon
 
 Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface.
 Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency.
-This is a selected subset of Gluon tutorials. For the comprehensive tutorial on Gluon,
-please see [gluon.mxnet.io](http://gluon.mxnet.io).
+
+This is a selected subset of Gluon tutorials that explains basic usage of Gluon and fundamental concepts in deep learning. For the comprehensive tutorial on Gluon that covers topics from basic statistics and probability theory to reinforcement learning and recommender systems, please see [gluon.mxnet.io](http://gluon.mxnet.io). 
 
 ### Basics
 
@@ -32,6 +29,8 @@ please see [gluon.mxnet.io](http://gluon.mxnet.io).
 
 ## MXNet
 
+These tutorials introduce a few fundamental concepts in deep learning and how to implement them in _MXNet_. The _Basics_ section contains tutorials on manipulating arrays, building networks, loading/preprocessing data, etc. The _Training and Inference_ section talks about implementing Linear Regression, training a Handwritten digit classifier using MLP and CNN, running inferences using a pre-trained model, and lastly, efficiently training a large scale image classifier.
+
 ### Basics
 
 ```eval_rst

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