diff --git a/doc/build/build_from_source.md b/doc/build/build_from_source.md
index a8de2c42d9a5700d4f5af924c3145fdc640aebdd..a191d31318aa67e6f4de75d81c031aeb729311c9 100644
--- a/doc/build/build_from_source.md
+++ b/doc/build/build_from_source.md
@@ -42,7 +42,7 @@ sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python
 sudo apt-get install libgoogle-glog-dev
 sudo apt-get install libgflags-dev
 sudo apt-get install libgtest-dev
-pip install wheel
+sudo pip install wheel
 pushd /usr/src/gtest
 cmake .
 make
diff --git a/doc/demo/quick_start/index_en.md b/doc/demo/quick_start/index_en.md
index 179da870e87e4354616a252921789288d0917054..ee3fa2a2166f497524663574270b239a6170ab19 100644
--- a/doc/demo/quick_start/index_en.md
+++ b/doc/demo/quick_start/index_en.md
@@ -59,7 +59,7 @@ To build your text classification system, your code will need to perform five st
 ## Preprocess data into standardized format
 In this example, you are going to use [Amazon electronic product review dataset](http://jmcauley.ucsd.edu/data/amazon/) to build a bunch of deep neural network models for text classification. Each text in this dataset is a product review. This dataset has two categories: “positive” and “negative”. Positive means the reviewer likes the product, while negative means the reviewer does not like the product.
 
-`demo/quick_start` in the source code provides scripts for downloading data and preprocessing data as shown below. The data process takes several minutes (about 3 minutes in our machine).
+`demo/quick_start` in the [source code](https://github.com/baidu/Paddle) provides scripts for downloading data and preprocessing data as shown below. The data process takes several minutes (about 3 minutes in our machine).
 
 ```bash
 cd demo/quick_start
diff --git a/doc_cn/demo/quick_start/index.md b/doc_cn/demo/quick_start/index.md
index 05089267b238ee5cf2350fdfa4dcc859bd41d6d0..34cd4a840e442c0ff2559bd8627a14d5388a0971 100644
--- a/doc_cn/demo/quick_start/index.md
+++ b/doc_cn/demo/quick_start/index.md
@@ -32,7 +32,7 @@
 
 ## 数据格式准备(Data Preparation)
 在本问题中，我们使用[Amazon电子产品评论数据](http://jmcauley.ucsd.edu/data/amazon/)，
-将评论分为好评(正样本)和差评(负样本)两类。源码的`demo/quick_start`里提供了数据下载脚本
+将评论分为好评(正样本)和差评(负样本)两类。[源码](https://github.com/baidu/Paddle)的`demo/quick_start`里提供了数据下载脚本
 和预处理脚本。
 
 ```bash
@@ -144,7 +144,7 @@ PyDataProviderWrapper</a>。
 
 我们将以基本的逻辑回归网络作为起点，并逐渐展示更加深入的功能。更详细的网络配置
 连接请参考<a href = "../../../doc/layer.html">Layer文档</a>。
-所有配置在源码`demo/quick_start`目录，首先列举逻辑回归网络。
+所有配置在[源码](https://github.com/baidu/Paddle)`demo/quick_start`目录，首先列举逻辑回归网络。
 
 ### 逻辑回归模型(Logistic Regression)