update readme

Author: zsdonghao

README.md

@@ -55,6 +55,14 @@ If you already had the pre-requisites ready (numpy, scipy, scikit-image, matplot
 [for stable version] pip install tensorlayer
 ```
 
+# Documentation
+
+The documentation [[Online]](http://tensorlayer.readthedocs.io/en/latest/) [[PDF]](https://media.readthedocs.org/pdf/tensorlayer/latest/tensorlayer.pdf) [[Epub]](http://readthedocs.org/projects/tensorlayer/downloads/epub/latest/) [[HTML]](http://readthedocs.org/projects/tensorlayer/downloads/htmlzip/latest/) describes the usages of TensorLayer APIs. It is also a self-contained document that walks through different types of deep neural networks, reinforcement learning and their applications in Natural Language Processing (NLP) problems. 
+
+We have included the corresponding modularized implementations of Google TensorFlow Deep Learning tutorial, so you can read the TensorFlow tutorial [[en]](https://www.tensorflow.org/versions/master/tutorials/index.html) [[cn]](http://wiki.jikexueyuan.com/project/tensorflow-zh/) along with our document.
+
+[Chinese documentation](http://tensorlayercn.readthedocs.io/zh/latest/) is also available.
+
 # Your First Program
 
 The first program trains a multi-layer perception network to solve the MNIST problem. We use the well-known  [scikit](http://scikit-learn.org/stable/)-style functions such as ``fit()`` and ``test()``. The program is self-explained.
@@ -122,13 +130,7 @@ We provide many helper functions (like `fit()` , `test()`) that is similar to Ke
 
 [Tricks to use TL](https://github.com/wagamamaz/tensorlayer-tricks) is also a good introduction to use TensorLayer.
 
-# Documentation
-
-The documentation [[Online]](http://tensorlayer.readthedocs.io/en/latest/) [[PDF]](https://media.readthedocs.org/pdf/tensorlayer/latest/tensorlayer.pdf) [[Epub]](http://readthedocs.org/projects/tensorlayer/downloads/epub/latest/) [[HTML]](http://readthedocs.org/projects/tensorlayer/downloads/htmlzip/latest/) describes the usages of TensorLayer APIs. It is also a self-contained document that walks through different types of deep neural networks, reinforcement learning and their applications in Natural Language Processing (NLP) problems. 
 
-We have included the corresponding modularized implementations of Google TensorFlow Deep Learning tutorial, so you can read the TensorFlow tutorial [[en]](https://www.tensorflow.org/versions/master/tutorials/index.html) [[cn]](http://wiki.jikexueyuan.com/project/tensorflow-zh/) along with our document.
-
-[Chinese documentation](http://tensorlayercn.readthedocs.io/zh/latest/) is also available.
 
 # More Examples