refine doc structure and revise some words

doc/about/index.rst

-About
+ABOUT
 =======
 
-
-Credits
---------
-
 PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform,
 which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
 
 PaddlePaddle is now open source but far from complete, which is intended to be built upon, improved, scaled, and extended.
 We hope to build an active open source community both by providing feedback and by actively contributing to the source code.
 
+
+Credits
+--------
+
 We owe many thanks to `all contributors and developers <https://github.com/PaddlePaddle/Paddle/blob/develop/authors>`_ of PaddlePaddle!

doc/api/data_provider/index.rst

-DataProvider Introduction
-=========================
+Introduction
+==============
 DataProvider is a module that loads training or testing data into cpu or gpu
 memory for the following triaining or testing process.
 

doc/api/data_provider/pydataprovider2.rst

-How to use PyDataProvider2
-==========================
+PyDataProvider2
+=================
 
 We highly recommand users to use PyDataProvider2 to provide training or testing
 data to PaddlePaddle. The user only needs to focus on how to read a single

doc/api/index.md

-# API
-
-## Data Provider
-
-* [Introduction](data_provider/index.rst)
-* [PyDataProvider2](data_provider/pydataprovider2.rst)
-
-## Trainer Configuration
-
-* [Model Config Interface](trainer_config_helpers/index.rst)
-
-## Predict
-
-* [Python Prediction API](predict/swig_py_paddle_en.rst)

doc/api/index.rst

+API
+====
+
+DataProvider API
+----------------
+
+..  toctree::
+  :maxdepth: 1
+
+  data_provider/index.rst
+  data_provider/pydataprovider2.rst
+
+Model Config API
+----------------
+
+..  toctree::
+  :maxdepth: 1
+
+  trainer_config_helpers/index.rst
+  trainer_config_helpers/optimizers.rst
+  trainer_config_helpers/data_sources.rst
+  trainer_config_helpers/layers.rst
+  trainer_config_helpers/activations.rst 
+  trainer_config_helpers/poolings.rst
+  trainer_config_helpers/networks.rst
+  trainer_config_helpers/evaluators.rst
+  trainer_config_helpers/attrs.rst
+
+
+Applications API
+----------------
+
+..  toctree::
+  :maxdepth: 1
+
+  predict/swig_py_paddle_en.rst
\ No newline at end of file

doc/api/predict/swig_py_paddle_en.rst

-Python Prediction API
-=====================
+Python Prediction
+==================
 
 PaddlePaddle offers a set of clean prediction interfaces for python with the help of
 SWIG. The main steps of predict values in python are:

doc/api/trainer_config_helpers/attrs.rst

-Parameter and Extra Layer Attribute
-===================================
+Parameter Attributes
+=======================
 
 ..  automodule:: paddle.trainer_config_helpers.attrs
     :members:

doc/api/trainer_config_helpers/index.rst

-Model Config Interface
-======================
-
-.. toctree::
-  :maxdepth: 1
-
-  optimizers.rst
-  data_sources.rst
-  layers.rst
-  activations.rst 
-  poolings.rst
-  networks.rst
-  evaluators.rst
-  attrs.rst

doc/introduction/index.rst → doc/getstarted/index.rst

-Introduction
+GET STARTED
 ============
 
 ..  toctree::

doc/howto/algorithm/rnn/bi_lstm.jpg

-../../../tutorials/sentiment_analysis/bi_lstm.jpg
\ No newline at end of file

doc/howto/algorithm/rnn/encoder-decoder-attention-model.png

-../../../tutorials/text_generation/encoder-decoder-attention-model.png
\ No newline at end of file

doc/howto/cluster/cluster_train.md

-# Distributed Training
+# How to Run Distributed Training
 
 In this article, we explain how to run distributed Paddle training jobs on clusters.  We will create the distributed version of the single-process training example, [recommendation](https://github.com/baidu/Paddle/tree/develop/demo/recommendation).
 

doc/howto/cmd_argument/index.md → doc/howto/cmd_parameter/index.md

-# Command Line Argument
+# How to Set Command-line Parameters
 
 * [Use Case](use_case.md)
-* [Argument Outline](argument_outline.md)
+* [Arguments](arguments.md)
 * [Detailed Descriptions](detail_introduction.md)

doc/howto/contribute_to_paddle.md

-# Contribute Code
+# How to Contribute Code
 
 We sincerely appreciate your contributions. You can use fork and pull request
 workflow to merge your code. 

doc/howto/algorithm/index.rst → doc/howto/deep_model/index.rst

-Algorithm Configuration
-=======================
+How to Configure Deep Models
+============================
 
 ..  toctree::
   :maxdepth: 1

doc/howto/algorithm/rnn/rnn.rst → doc/howto/deep_model/rnn/rnn.rst

@@ -42,7 +42,7 @@ Simple Gated Recurrent Neural Network
 
 Recurrent neural network process a sequence at each time step sequentially. An example of the architecture of LSTM is listed below.
 
-.. image:: ./bi_lstm.jpg
+.. image:: ../../../tutorials/sentiment_analysis/bi_lstm.jpg
 	 :align: center
 
 Generally speaking, a recurrent network perform the following operations from :math:`t=1` to :math:`t=T`, or reversely from :math:`t=T` to :math:`t=1`.
@@ -101,7 +101,7 @@ Sequence to Sequence Model with Attention
 -----------------------------------------
 We will use the sequence to sequence model with attention as an example to demonstrate how you can configure complex recurrent neural network models. An illustration of the sequence to sequence model with attention is shown in the following figure.
 
-.. image:: ./encoder-decoder-attention-model.png
+.. image:: ../../../tutorials/text_generation/encoder-decoder-attention-model.png
  	 :align: center
 
 In this model, the source sequence :math:`S = \{s_1, \dots, s_T\}` is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network :math:`H_S = \{H_1, \dots, H_T\}` is called *encoder vector* The decoder is a gated recurrent neural network. When decoding each token :math:`y_t`, the gated recurrent neural network generates a set of weights :math:`W_S^t = \{W_1^t, \dots, W_T^t\}`, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token :math:`y_t`.

doc/howto/dev/index.rst

-Development Guide
-=================
-
-..  toctree::
-  :maxdepth: 2
-
-  layer.md
-  new_layer/new_layer.rst
-  source/index.rst

doc/howto/dev/layer.md

-# Layer Documents
-
-* [Layer Python API](../../api/trainer_config_helpers/index.rst)
-* [Layer Source Code](source/gserver/layers.rst)
-* [Writing New Layers](new_layer/new_layer.rst)

doc/howto/index.rst

-How to
+HOW TO
 =======
 
+Usage
+-------
+
 ..  toctree::
   :maxdepth: 1
 
-  cmd_argument/index.md
+  cmd_parameter/index.md
+  deep_model/index.rst
   cluster/cluster_train.md
-  algorithm/index.rst
+
+Development
+------------
+
+..  toctree::
+  :maxdepth: 1
+
+  new_layer/index.rst
+  contribute_to_paddle.md
+
+Optimization
+-------------
+
+..  toctree::
+  :maxdepth: 1
+
   optimization/index.rst
-  dev/index.rst
-  contribute_to_paddle.md
\ No newline at end of file

doc/howto/dev/new_layer/new_layer.rst → doc/howto/new_layer/index.rst

-==================
-Writing New Layers
-==================
+=======================
+How to Write New Layers
+=======================
 
 This tutorial will guide you to write customized layers in PaddlePaddle. We will utilize fully connected layer as an example to guide you through the following steps for writing a new layer.
 

doc/howto/optimization/index.rst

-Performance Tuning
-==================
+How to Tune GPU Performance
+===========================
 
 .. toctree::
   :maxdepth: 3

doc/index.rst

@@ -4,7 +4,7 @@ PaddlePaddle Documentation
 ..  toctree::
   :maxdepth: 1
 
-  introduction/index.rst
+  getstarted/index.rst
   tutorials/index.md
   howto/index.rst
   api/index.rst

doc/tutorials/index.md

-# Tutorials
+# TUTORIALS
 There are serveral examples and demos here.
 
 ## Image