follow comments2

doc/getstarted/build_and_install/build_from_source_cn.rst

@@ -103,10 +103,10 @@ PaddlePaddle可以使用cuDNN v5.1之后的任何一个版本来编译运行，
 编译选项的设置
 ++++++++++++++
 
-PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。cmake编译时，首先在系统路径(/usr/lib\:/usr/local/lib)中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如 
+PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。cmake编译时，首先在系统路径（ :code:`/usr/lib:/usr/local/lib` ）中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如 
 
 ..  code-block:: bash
 
     cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5
 
-注意：这几个编译选项的设置，只在第一次cmake的时候有效。如果之后想要重新设置，推荐清理整个编译目录（``rm -rf``）后，再指定。
+**注意：这几个编译选项的设置，只在第一次cmake的时候有效。如果之后想要重新设置，推荐清理整个编译目录（** :code:`rm -rf` ）**后，再指定。**

doc/getstarted/build_and_install/build_from_source_en.rst

@@ -115,12 +115,11 @@ Pass Compile Options
 
 You can pass compile options to use intended BLAS/CUDA/Cudnn libraries.
 When running cmake command, it will search system paths like
-:code:`/usr/lib\:/usr/local/lib` and then search paths that you
+:code:`/usr/lib:/usr/local/lib` and then search paths that you
 passed to cmake, i.e.
 
 ..  code-block:: bash
 
     cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5
 
-**NOTE: These options only take effect when running cmake for the first time, you need to clean the cmake cache or clean the build directory if you want to change it.**
-
+**NOTE: These options only take effect when running cmake for the first time, you need to clean the cmake cache or clean the build directory (** :code:`rm -rf` **) if you want to change it.**

doc/getstarted/build_and_install/docker_install_cn.rst

@@ -77,7 +77,7 @@
      cd /work
      python train.py
 
-**注：PaddlePaddle Docker镜像为了减小体积，默认没有安装vim，您可以在容器中执行 :code:`apt-get install -y vim` 安装后，在容器中编辑代码。**
+**注：PaddlePaddle Docker镜像为了减小体积，默认没有安装vim，您可以在容器中执行** :code:`apt-get install -y vim` **安装后，在容器中编辑代码。**
 
 .. _docker_run_book:
 

doc/getstarted/build_and_install/docker_install_en.rst

@@ -84,7 +84,7 @@ interactively:
      cd /work
      python train.py
 
-**NOTE: We did not install vim in the default docker image to reduce the image size, you can run :code:`apt-get install -y vim` to install it if you need to edit python files.**
+**NOTE: We did not install vim in the default docker image to reduce the image size, you can run** :code:`apt-get install -y vim` **to install it if you need to edit python files.**
 
 .. _docker_run_book:
 

doc/getstarted/build_and_install/pip_install_cn.rst

@@ -27,6 +27,10 @@ PaddlePaddle可以使用常用的Python包管理工具
 如果需要获取并安装最新的（开发分支）PaddlePaddle，可以从我们的CI系统中下载最新的whl安装包和c-api开发包并安装，
 您可以从下面的表格中找到需要的版本：
 
+如果在点击下面链接时出现如下登陆界面，点击“Log in as guest”即可开始下载：
+
+.. image:: paddleci.png
+
 ..  csv-table:: 各个版本最新的whl包
     :header: "版本说明", "cp27-cp27mu", "cp27-cp27mu", "C-API"
     :widths: 1, 3, 3, 3

doc/getstarted/build_and_install/pip_install_en.rst

@@ -30,6 +30,10 @@ you can download the latest whl package from our CI system. Access
 the below links, log in as guest, then click at the "Artifact"
 tab, you'll find the download link of whl packages.
 
+If the links below shows up the login form, just click "Log in as guest" to start the download:
+
+.. image:: paddleci.png
+
 ..  csv-table:: whl package of each version
     :header: "version", "cp27-cp27mu", "cp27-cp27mu", "C-API"
     :widths: 1, 3, 3, 3
@@ -46,7 +50,7 @@ Runtime Dependency
 ------------------------------
 
 PaddlePaddle installation packages (whl) does not only contain .py files,
-but also binaries built from C++ code, we ensure that PaddlePaddle can
+but also binaries built from C++ code. We ensure that PaddlePaddle can
 run on current mainline Linux distributions, like CentOS 6, Ubuntu 14.04
 and MacOS 10.12.
 

doc/getstarted/index_cn.rst

@@ -31,9 +31,7 @@ PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.
 快速开始
 ++++++++
 
-下载 `房价模型文件 <https://raw.githubusercontent.com/PaddlePaddle/book/develop/01.fit_a_line/fit_a_line.tar>`_
-
-创建一个 housing.py 并粘贴此Python代码 (请确保fit_a_line.tar 是在正确的路径上)
+创建一个 housing.py 并粘贴此Python代码：
 
   .. code-block:: python
 
@@ -46,16 +44,14 @@ PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.
      x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
      y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
 
-     with open('fit_a_line.tar', 'r') as f:
-         parameters = paddle.parameters.Parameters.from_tar(f)
-
      # Infer using provided test data.
      probs = paddle.infer(
-          output_layer=y_predict, parameters=parameters,
-          input=[item for item in paddle.dataset.uci_housing.test()()])
+         output_layer=y_predict,
+         parameters=paddle.dataset.uci_housing.model(),
+         input=[item for item in paddle.dataset.uci_housing.test()()])
 
      for i in xrange(len(probs)):
-          print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
+         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
 
 执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。
 

doc/getstarted/index_en.rst

@@ -6,7 +6,7 @@ GET STARTED
 Quick Install
 ----------------------
 
-You can use pip to install PaddlePaddle using a single command, supports
+You can use pip to install PaddlePaddle with a single command, supports
 CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
 Simply run the following command to install:
 
@@ -33,11 +33,8 @@ For more details about installation and build:
 Quick Start
 ++++++++
 
-Download the `trained housing prices model <https://raw.githubusercontent.com/PaddlePaddle/book/develop/01.fit_a_line/fit_a_line.tar>`_
-
-Now, create a new file called housing.py, and paste this Python
-code (make sure to set the right path based on the location of fit_a_line.tar
-on your computer):
+Create a new file called housing.py, and paste this Python
+code:
 
 
   .. code-block:: python
@@ -51,16 +48,14 @@ on your computer):
      x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
      y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
 
-     with open('fit_a_line.tar', 'r') as f:
-         parameters = paddle.parameters.Parameters.from_tar(f)
-
      # Infer using provided test data.
      probs = paddle.infer(
-          output_layer=y_predict, parameters=parameters,
-          input=[item for item in paddle.dataset.uci_housing.test()()])
+         output_layer=y_predict,
+         parameters=paddle.dataset.uci_housing.model(),
+         input=[item for item in paddle.dataset.uci_housing.test()()])
 
      for i in xrange(len(probs)):
-          print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
+         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
 
 Run :code:`python housing.py` and voila! It should print out a list of predictions
 for the test housing data.