diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f77a171c1b28663adfe52fce876168b04a04cfb5..8359afe36571b8335afe2f528dc08285e4efce48 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -26,9 +26,11 @@
     -   id: remove-tabs
         files: \.md$
 -   repo: https://github.com/reyoung/pre-commit-hooks-jinja-compile.git
-    sha: 85ad800cbc9c60a64230d60971aa9576fd57e508
+    sha: 4a369cc72a4a2b8d3813ab8cc17abb5f5b21ef6c
     hooks:
     -   id: convert-jinja2-into-html
+        # The argument means repleace filename from pattern `.*/([^/]*)\.tmpl` to `\1`
+        args: ['--filename_pattern=.*/([^/]*)\.tmpl', '--filename_repl=\1']
 -   repo: local
     hooks:
     -   id: convert-markdown-into-html
diff --git a/.pre-commit-hooks/convert_markdown_into_html.py b/.pre-commit-hooks/convert_markdown_into_html.py
index acb3d0ad0559b35a6b1f86283c1193c8935c5caa..66f44ef23c5d9a82436dfbe4b6bcdfc4e69ab55a 100644
--- a/.pre-commit-hooks/convert_markdown_into_html.py
+++ b/.pre-commit-hooks/convert_markdown_into_html.py
@@ -18,13 +18,13 @@ HEAD = """
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
diff --git a/index.en.html.json b/.tools/templates/index.en.html.json
similarity index 100%
rename from index.en.html.json
rename to .tools/templates/index.en.html.json
diff --git a/index.en.html.tmpl b/.tools/templates/index.en.html.tmpl
similarity index 100%
rename from index.en.html.tmpl
rename to .tools/templates/index.en.html.tmpl
diff --git a/index.html.json b/.tools/templates/index.html.json
similarity index 100%
rename from index.html.json
rename to .tools/templates/index.html.json
diff --git a/index.html.tmpl b/.tools/templates/index.html.tmpl
similarity index 98%
rename from index.html.tmpl
rename to .tools/templates/index.html.tmpl
index 3729ec62426c393c08afb934fcfc2d61832cfcd3..2762bc0a7bb15f0260d35d9571429171d972657c 100644
--- a/index.html.tmpl
+++ b/.tools/templates/index.html.tmpl
@@ -11,6 +11,7 @@
 * {
     font-family:"Roboto","Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;
 }
+
 .left-panel {
     background: #E5E6EA;
 }
@@ -68,7 +69,7 @@
 <body>
     <nav class="navbar navbar-toggleable-md navbar-inverse bg-inverse">
         <a class="navbar-brand mr-auto" href="#">
-            <img alt="PaddlePaddle" src="./.theme/PP_w.png">
+            <img alt="PaddlePaddle" src="./.tools/theme/PP_w.png">
         </a>
         <ul class="nav navbar-nav">
           <li class="nav-item">
diff --git a/.theme/PP_w.png b/.tools/theme/PP_w.png
similarity index 100%
rename from .theme/PP_w.png
rename to .tools/theme/PP_w.png
diff --git a/.tmpl/github-markdown.css b/.tools/theme/github-markdown.css
similarity index 100%
rename from .tmpl/github-markdown.css
rename to .tools/theme/github-markdown.css
diff --git a/.tmpl/marked.js b/.tools/theme/marked.js
similarity index 100%
rename from .tmpl/marked.js
rename to .tools/theme/marked.js
diff --git a/01.fit_a_line/README.en.ipynb b/01.fit_a_line/README.en.ipynb
index 8330e57eed76b07f57422ffde75b66619e8f027b..a3f95c5ca96a7e6149c474d2ebb2c1d958bbe546 100644
--- a/01.fit_a_line/README.en.ipynb
+++ b/01.fit_a_line/README.en.ipynb
@@ -7,7 +7,7 @@
         "# Linear Regression\n",
         "Let us begin the tutorial with a classical problem called Linear Regression \\[[1](#References)\\]. In this chapter, we will train a model from a realistic dataset to predict home prices. Some important concepts in Machine Learning will be covered through this example.\n",
         "\n",
-        "The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.en.md).\n",
+        "The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).\n",
         "\n",
         "## Problem Setup\n",
         "Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity.\n",
diff --git a/01.fit_a_line/README.en.md b/01.fit_a_line/README.en.md
index 4d4d0a2b2cce7a2d1ce725a9c97d571f31398000..7f42b7da50adec54a779d388cec6572177dd88f3 100644
--- a/01.fit_a_line/README.en.md
+++ b/01.fit_a_line/README.en.md
@@ -1,7 +1,7 @@
 # Linear Regression
 Let us begin the tutorial with a classical problem called Linear Regression \[[1](#References)\]. In this chapter, we will train a model from a realistic dataset to predict home prices. Some important concepts in Machine Learning will be covered through this example.
 
-The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.en.md).
+The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.en.md).
 
 ## Problem Setup
 Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity.
diff --git a/01.fit_a_line/README.ipynb b/01.fit_a_line/README.ipynb
index 38b23e6e3ade95aedb87404a6d517674fc9431a0..78666779bbf83d8469f18ab5d8cba17fd664919f 100644
--- a/01.fit_a_line/README.ipynb
+++ b/01.fit_a_line/README.ipynb
@@ -7,7 +7,7 @@
         "# 线性回归\n",
         "让我们从经典的线性回归（Linear Regression \\[[1](#参考文献)\\]）模型开始这份教程。在这一章里，你将使用真实的数据集建立起一个房价预测模型，并且了解到机器学习中的若干重要概念。\n",
         "\n",
-        "本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.md)。\n",
+        "本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。\n",
         "\n",
         "## 背景介绍\n",
         "给定一个大小为$n$的数据集  ${\\{y_{i}, x_{i1}, ..., x_{id}\\}}_{i=1}^{n}$，其中$x_{i1}, \\ldots, x_{id}$是第$i$个样本$d$个属性上的取值，$y_i$是该样本待预测的目标。线性回归模型假设目标$y_i$可以被属性间的线性组合描述，即\n",
diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md
index 56fecf7a1ac5f0bfb51dab2cfe75473c49f370d7..2c3f1fd4dd61aec8ad6e97881d6229f4e6ef91b1 100644
--- a/01.fit_a_line/README.md
+++ b/01.fit_a_line/README.md
@@ -1,7 +1,7 @@
 # 线性回归
 让我们从经典的线性回归（Linear Regression \[[1](#参考文献)\]）模型开始这份教程。在这一章里，你将使用真实的数据集建立起一个房价预测模型，并且了解到机器学习中的若干重要概念。
 
-本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.md)。
+本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.md)。
 
 ## 背景介绍
 给定一个大小为$n$的数据集  ${\{y_{i}, x_{i1}, ..., x_{id}\}}_{i=1}^{n}$，其中$x_{i1}, \ldots, x_{id}$是第$i$个样本$d$个属性上的取值，$y_i$是该样本待预测的目标。线性回归模型假设目标$y_i$可以被属性间的线性组合描述，即
diff --git a/01.fit_a_line/index.en.html b/01.fit_a_line/index.en.html
index 7dd703d3fc8bbd43d36b079a4098cd87a2d5edbe..06f5559174c252eaf951211d86ce86c7897d9ed9 100644
--- a/01.fit_a_line/index.en.html
+++ b/01.fit_a_line/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -43,7 +43,7 @@
 # Linear Regression
 Let us begin the tutorial with a classical problem called Linear Regression \[[1](#References)\]. In this chapter, we will train a model from a realistic dataset to predict home prices. Some important concepts in Machine Learning will be covered through this example.
 
-The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.en.md).
+The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/book/blob/develop/README.en.md).
 
 ## Problem Setup
 Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity.
diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html
index a45eee746081a88b3c64d3bbf64fd36dc5d6bda8..a866e13bf3321c22973198d8ae639327348c4587 100644
--- a/01.fit_a_line/index.html
+++ b/01.fit_a_line/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -43,7 +43,7 @@
 # 线性回归
 让我们从经典的线性回归（Linear Regression \[[1](#参考文献)\]）模型开始这份教程。在这一章里，你将使用真实的数据集建立起一个房价预测模型，并且了解到机器学习中的若干重要概念。
 
-本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.md)。
+本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/01.fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.md)。
 
 ## 背景介绍
 给定一个大小为$n$的数据集  ${\{y_{i}, x_{i1}, ..., x_{id}\}}_{i=1}^{n}$，其中$x_{i1}, \ldots, x_{id}$是第$i$个样本$d$个属性上的取值，$y_i$是该样本待预测的目标。线性回归模型假设目标$y_i$可以被属性间的线性组合描述，即
diff --git a/02.recognize_digits/README.en.md b/02.recognize_digits/README.en.md
index 98ce6158439f61d1e5a4337c58f3e6027fa8fea7..15a70e75af4caa39f32d7b4db4233fc080a17eed 100644
--- a/02.recognize_digits/README.en.md
+++ b/02.recognize_digits/README.en.md
@@ -1,6 +1,6 @@
 # Recognize Digits
 
-The source code for this tutorial is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits). First-time readers, please refer to PaddlePaddle [installation instructions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
+The source code for this tutorial is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). First-time readers, please refer to PaddlePaddle [installation instructions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
 ## Introduction
 When we learn a new programming language, the first task is usually to write a program that prints "Hello World." In Machine Learning or Deep Learning, the equivalent task is to train a model to perform handwritten digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a 28x28 matrix, and the label is one of the digits from 0 to 9. Each image is normalized in size and centered.
@@ -12,7 +12,7 @@ Fig. 1. Examples of MNIST images
 
 The MNIST dataset is created from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students the in U.S. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set (60,000 samples) and test set (10,000 samples), where training set was labeled by 250 different annotators, and it was guaranteed that there wasn't a complete overlap of annotators of training set and test set.
 
-Yann LeCun, one of the founders of Deep Learning, contributed highly towards handwritten character recognition in early days and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From Yann LeCun's first proposal of LeNet to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification) tutorial), CNN achieved a series of impressive results in Image Classification tasks.
+Yann LeCun, one of the founders of Deep Learning, contributed highly towards handwritten character recognition in early days and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From Yann LeCun's first proposal of LeNet to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNN achieved a series of impressive results in Image Classification tasks.
 
 Many algorithms are tested on MNIST. In 1998, LeCun experimented with single layer linear classifier, MLP (Multilayer Perceptron) and Multilayer CNN LeNet. These algorithms constantly reduced test error from 12% to 0.7% \[[1](#References)\]. Since then, researchers have worked on many algorithms such as k-NN (K-Nearest Neighbors) \[[2](#References)\], Support Vector Machine (SVM) \[[3](#References)\], Neural Networks \[[4-7](#References)\] and Boosting \[[8](#References)\]. Various preprocessing methods like distortion removal, noise removal, blurring etc. have also been applied to increase recognition accuracy.
 
diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md
index 0b692282b157e879791942838a9a693e58c45fff..85f0defa1989b3832a50564e46274ae7270bed60 100644
--- a/02.recognize_digits/README.md
+++ b/02.recognize_digits/README.md
@@ -1,6 +1,6 @@
 # 识别数字
 
-本教程源代码目录在[book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 当我们学习编程的时候，编写的第一个程序一般是实现打印"Hello World"。而机器学习（或深度学习）的入门教程，一般都是 [MNIST](http://yann.lecun.com/exdb/mnist/) 数据库上的手写识别问题。原因是手写识别属于典型的图像分类问题，比较简单，同时MNIST数据集也很完备。MNIST数据集作为一个简单的计算机视觉数据集，包含一系列如图1所示的手写数字图片和对应的标签。图片是28x28的像素矩阵，标签则对应着0~9的10个数字。每张图片都经过了大小归一化和居中处理。
@@ -12,7 +12,7 @@
 
 MNIST数据集是从 [NIST](https://www.nist.gov/srd/nist-special-database-19) 的Special Database 3（SD-3）和Special Database 1（SD-1）构建而来。由于SD-3是由美国人口调查局的员工进行标注，SD-1是由美国高中生进行标注，因此SD-3比SD-1更干净也更容易识别。Yann LeCun等人从SD-1和SD-3中各取一半作为MNIST的训练集（60000条数据）和测试集（10000条数据），其中训练集来自250位不同的标注员，此外还保证了训练集和测试集的标注员是不完全相同的。
 
-Yann LeCun早先在手写字符识别上做了很多研究，并在研究过程中提出了卷积神经网络（Convolutional Neural Network），大幅度地提高了手写字符的识别能力，也因此成为了深度学习领域的奠基人之一。如今的深度学习领域，卷积神经网络占据了至关重要的地位，从最早Yann LeCun提出的简单LeNet，到如今ImageNet大赛上的优胜模型VGGNet、GoogLeNet、ResNet等（请参见[图像分类](https://github.com/PaddlePaddle/book/tree/develop/image_classification) 教程），人们在图像分类领域，利用卷积神经网络得到了一系列惊人的结果。
+Yann LeCun早先在手写字符识别上做了很多研究，并在研究过程中提出了卷积神经网络（Convolutional Neural Network），大幅度地提高了手写字符的识别能力，也因此成为了深度学习领域的奠基人之一。如今的深度学习领域，卷积神经网络占据了至关重要的地位，从最早Yann LeCun提出的简单LeNet，到如今ImageNet大赛上的优胜模型VGGNet、GoogLeNet、ResNet等（请参见[图像分类](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) 教程），人们在图像分类领域，利用卷积神经网络得到了一系列惊人的结果。
 
 有很多算法在MNIST上进行实验。1998年，LeCun分别用单层线性分类器、多层感知器（Multilayer Perceptron, MLP）和多层卷积神经网络LeNet进行实验，使得测试集上的误差不断下降（从12%下降到0.7%）\[[1](#参考文献)\]。此后，科学家们又基于K近邻（K-Nearest Neighbors）算法\[[2](#参考文献)\]、支持向量机（SVM）\[[3](#参考文献)\]、神经网络\[[4-7](#参考文献)\]和Boosting方法\[[8](#参考文献)\]等做了大量实验，并采用多种预处理方法（如去除歪曲、去噪、模糊等）来提高识别的准确率。
 
diff --git a/02.recognize_digits/index.en.html b/02.recognize_digits/index.en.html
index 62b49dfdd6442858ff13593e5f77d9d2deba518d..03960fd4b38172a6b821c9b612a7d7c2034bd8a2 100644
--- a/02.recognize_digits/index.en.html
+++ b/02.recognize_digits/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
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@@ -42,7 +42,7 @@
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 # Recognize Digits
 
-The source code for this tutorial is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits). First-time readers, please refer to PaddlePaddle [installation instructions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
+The source code for this tutorial is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). First-time readers, please refer to PaddlePaddle [installation instructions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
 ## Introduction
 When we learn a new programming language, the first task is usually to write a program that prints "Hello World." In Machine Learning or Deep Learning, the equivalent task is to train a model to perform handwritten digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a 28x28 matrix, and the label is one of the digits from 0 to 9. Each image is normalized in size and centered.
@@ -54,7 +54,7 @@ Fig. 1. Examples of MNIST images
 
 The MNIST dataset is created from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students the in U.S. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set (60,000 samples) and test set (10,000 samples), where training set was labeled by 250 different annotators, and it was guaranteed that there wasn't a complete overlap of annotators of training set and test set.
 
-Yann LeCun, one of the founders of Deep Learning, contributed highly towards handwritten character recognition in early days and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From Yann LeCun's first proposal of LeNet to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification) tutorial), CNN achieved a series of impressive results in Image Classification tasks.
+Yann LeCun, one of the founders of Deep Learning, contributed highly towards handwritten character recognition in early days and proposed CNN (Convolutional Neural Network), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From Yann LeCun's first proposal of LeNet to those winning models in ImageNet, such as VGGNet, GoogLeNet, ResNet, etc. (Please refer to [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNN achieved a series of impressive results in Image Classification tasks.
 
 Many algorithms are tested on MNIST. In 1998, LeCun experimented with single layer linear classifier, MLP (Multilayer Perceptron) and Multilayer CNN LeNet. These algorithms constantly reduced test error from 12% to 0.7% \[[1](#References)\]. Since then, researchers have worked on many algorithms such as k-NN (K-Nearest Neighbors) \[[2](#References)\], Support Vector Machine (SVM) \[[3](#References)\], Neural Networks \[[4-7](#References)\] and Boosting \[[8](#References)\]. Various preprocessing methods like distortion removal, noise removal, blurring etc. have also been applied to increase recognition accuracy.
 
diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html
index 8df2d5846662a6b83789e4fbbcde3f6d630e4eb1..3b45d841952289b2fcd11ae149538596a5dd6a1f 100644
--- a/02.recognize_digits/index.html
+++ b/02.recognize_digits/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
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+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
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   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
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 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # 识别数字
 
-本教程源代码目录在[book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 当我们学习编程的时候，编写的第一个程序一般是实现打印"Hello World"。而机器学习（或深度学习）的入门教程，一般都是 [MNIST](http://yann.lecun.com/exdb/mnist/) 数据库上的手写识别问题。原因是手写识别属于典型的图像分类问题，比较简单，同时MNIST数据集也很完备。MNIST数据集作为一个简单的计算机视觉数据集，包含一系列如图1所示的手写数字图片和对应的标签。图片是28x28的像素矩阵，标签则对应着0~9的10个数字。每张图片都经过了大小归一化和居中处理。
@@ -54,7 +54,7 @@
 
 MNIST数据集是从 [NIST](https://www.nist.gov/srd/nist-special-database-19) 的Special Database 3（SD-3）和Special Database 1（SD-1）构建而来。由于SD-3是由美国人口调查局的员工进行标注，SD-1是由美国高中生进行标注，因此SD-3比SD-1更干净也更容易识别。Yann LeCun等人从SD-1和SD-3中各取一半作为MNIST的训练集（60000条数据）和测试集（10000条数据），其中训练集来自250位不同的标注员，此外还保证了训练集和测试集的标注员是不完全相同的。
 
-Yann LeCun早先在手写字符识别上做了很多研究，并在研究过程中提出了卷积神经网络（Convolutional Neural Network），大幅度地提高了手写字符的识别能力，也因此成为了深度学习领域的奠基人之一。如今的深度学习领域，卷积神经网络占据了至关重要的地位，从最早Yann LeCun提出的简单LeNet，到如今ImageNet大赛上的优胜模型VGGNet、GoogLeNet、ResNet等（请参见[图像分类](https://github.com/PaddlePaddle/book/tree/develop/image_classification) 教程），人们在图像分类领域，利用卷积神经网络得到了一系列惊人的结果。
+Yann LeCun早先在手写字符识别上做了很多研究，并在研究过程中提出了卷积神经网络（Convolutional Neural Network），大幅度地提高了手写字符的识别能力，也因此成为了深度学习领域的奠基人之一。如今的深度学习领域，卷积神经网络占据了至关重要的地位，从最早Yann LeCun提出的简单LeNet，到如今ImageNet大赛上的优胜模型VGGNet、GoogLeNet、ResNet等（请参见[图像分类](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) 教程），人们在图像分类领域，利用卷积神经网络得到了一系列惊人的结果。
 
 有很多算法在MNIST上进行实验。1998年，LeCun分别用单层线性分类器、多层感知器（Multilayer Perceptron, MLP）和多层卷积神经网络LeNet进行实验，使得测试集上的误差不断下降（从12%下降到0.7%）\[[1](#参考文献)\]。此后，科学家们又基于K近邻（K-Nearest Neighbors）算法\[[2](#参考文献)\]、支持向量机（SVM）\[[3](#参考文献)\]、神经网络\[[4-7](#参考文献)\]和Boosting方法\[[8](#参考文献)\]等做了大量实验，并采用多种预处理方法（如去除歪曲、去噪、模糊等）来提高识别的准确率。
 
diff --git a/03.image_classification/README.en.md b/03.image_classification/README.en.md
index db73fb6951a2c586ff8a29371b9055c82b59a069..0bb0775eaa3f689b4646e854cf5d6dd9f6d72263 100644
--- a/03.image_classification/README.en.md
+++ b/03.image_classification/README.en.md
@@ -1,7 +1,7 @@
 Image Classification
 =======================
 
-The source code for this chapter is at [book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification). First-time users, please refer to PaddlePaddle [Installation Tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) for installation instructions.
+The source code for this chapter is at [book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification). First-time users, please refer to PaddlePaddle [Installation Tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) for installation instructions.
 
 ## Background
 
diff --git a/03.image_classification/README.md b/03.image_classification/README.md
index 6236f13b0269d9495a0c415ff9e6ec7d92d1b1fa..e97cb3365591320bef7c2dd167a7b81fd79e46be 100644
--- a/03.image_classification/README.md
+++ b/03.image_classification/README.md
@@ -1,6 +1,6 @@
 # 图像分类
 
-本教程源代码目录在[book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/03.image_classification/index.en.html b/03.image_classification/index.en.html
index 5a870bcfd69bd62423fc422a60da16f463334b7b..4e4b413ab81a5b14702b2338040f2ad60fc27f33 100644
--- a/03.image_classification/index.en.html
+++ b/03.image_classification/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -43,7 +43,7 @@
 Image Classification
 =======================
 
-The source code for this chapter is at [book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification). First-time users, please refer to PaddlePaddle [Installation Tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) for installation instructions.
+The source code for this chapter is at [book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification). First-time users, please refer to PaddlePaddle [Installation Tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) for installation instructions.
 
 ## Background
 
diff --git a/03.image_classification/index.html b/03.image_classification/index.html
index 834b6506026f97cc6fa08fde070353ad54f8020c..6c178be6bb8e4ae60ba033be1505101413636128 100644
--- a/03.image_classification/index.html
+++ b/03.image_classification/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # 图像分类
 
-本教程源代码目录在[book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/04.word2vec/README.en.md b/04.word2vec/README.en.md
index 4c1ba71137e21d2a980bc0fda1ac3be6b158b7e2..53cdd509bd064c352207f30129586d792a0862e2 100644
--- a/04.word2vec/README.en.md
+++ b/04.word2vec/README.en.md
@@ -1,6 +1,6 @@
 # Word2Vec
 
-This is intended as a reference tutorial. The source code of this tutorial lives on [book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/word2vec).
+This is intended as a reference tutorial. The source code of this tutorial lives on [book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
diff --git a/04.word2vec/README.md b/04.word2vec/README.md
index eaf1feb77417307f9cb925240bad836631f22e09..872aeec60aed997d6cd54c9ccaa8f72b2b8b30cf 100644
--- a/04.word2vec/README.md
+++ b/04.word2vec/README.md
@@ -1,7 +1,7 @@
 
 # 词向量
 
-本教程源代码目录在[book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/word2vec)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/04.word2vec/index.en.html b/04.word2vec/index.en.html
index 1a4940012a583d422238336b712c8342b7a023eb..307c72f430735d188e2078f43b09ed11e9edca66 100644
--- a/04.word2vec/index.en.html
+++ b/04.word2vec/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # Word2Vec
 
-This is intended as a reference tutorial. The source code of this tutorial lives on [book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/word2vec).
+This is intended as a reference tutorial. The source code of this tutorial lives on [book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
diff --git a/04.word2vec/index.html b/04.word2vec/index.html
index ebc7d4f47d8bfb783be55df2723d7b057537506d..8fa126f5a8dd0497208cfd05b05e0faf6d18686e 100644
--- a/04.word2vec/index.html
+++ b/04.word2vec/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -43,7 +43,7 @@
 
 # 词向量
 
-本教程源代码目录在[book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/word2vec)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/word2vec](https://github.com/PaddlePaddle/book/tree/develop/04.word2vec)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/05.understand_sentiment/README.en.md b/05.understand_sentiment/README.en.md
index 5db0347208729a71986d6dc14e97bc5d8f0bc3a5..c1290e953302fe2d24f65fc2842a6676aa3b2436 100644
--- a/05.understand_sentiment/README.en.md
+++ b/05.understand_sentiment/README.en.md
@@ -1,6 +1,6 @@
 # Sentiment Analysis
 
-The source codes of this section can be located at [book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment). First-time users may refer to PaddlePaddle for [Installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
+The source codes of this section can be located at [book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). First-time users may refer to PaddlePaddle for [Installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
 ## Background
 
diff --git a/05.understand_sentiment/README.md b/05.understand_sentiment/README.md
index 3da65a419c0bc4146d2c2b55c4d446ef93706a8d..88978fed8b2af5c5b92d5a1019a3f9fc60e1bcdc 100644
--- a/05.understand_sentiment/README.md
+++ b/05.understand_sentiment/README.md
@@ -1,6 +1,6 @@
 # 情感分析
 
-本教程源代码目录在[book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 在自然语言处理中，情感分析一般是指判断一段文本所表达的情绪状态。其中，一段文本可以是一个句子，一个段落或一个文档。情绪状态可以是两类，如（正面，负面），（高兴，悲伤）；也可以是三类，如（积极，消极，中性）等等。情感分析的应用场景十分广泛，如把用户在购物网站（亚马逊、天猫、淘宝等）、旅游网站、电影评论网站上发表的评论分成正面评论和负面评论；或为了分析用户对于某一产品的整体使用感受，抓取产品的用户评论并进行情感分析等等。表格1展示了对电影评论进行情感分析的例子：
diff --git a/05.understand_sentiment/index.en.html b/05.understand_sentiment/index.en.html
index d64b42bea01afa58e6402d01f4fd457a80c91c84..0f48fc287e1834729251bc1628c525acc0c4dc5e 100644
--- a/05.understand_sentiment/index.en.html
+++ b/05.understand_sentiment/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # Sentiment Analysis
 
-The source codes of this section can be located at [book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment). First-time users may refer to PaddlePaddle for [Installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
+The source codes of this section can be located at [book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). First-time users may refer to PaddlePaddle for [Installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
 ## Background
 
diff --git a/05.understand_sentiment/index.html b/05.understand_sentiment/index.html
index 6340f3b509def6d132942b8d95a63c36d5caa718..b8db650d98e7afd9d876f156fdfa6388a1895a51 100644
--- a/05.understand_sentiment/index.html
+++ b/05.understand_sentiment/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # 情感分析
 
-本教程源代码目录在[book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 在自然语言处理中，情感分析一般是指判断一段文本所表达的情绪状态。其中，一段文本可以是一个句子，一个段落或一个文档。情绪状态可以是两类，如（正面，负面），（高兴，悲伤）；也可以是三类，如（积极，消极，中性）等等。情感分析的应用场景十分广泛，如把用户在购物网站（亚马逊、天猫、淘宝等）、旅游网站、电影评论网站上发表的评论分成正面评论和负面评论；或为了分析用户对于某一产品的整体使用感受，抓取产品的用户评论并进行情感分析等等。表格1展示了对电影评论进行情感分析的例子：
diff --git a/06.label_semantic_roles/README.en.md b/06.label_semantic_roles/README.en.md
index bf012416c45df0f839f5bb755a7561261a1b299d..f40269c3a3e93d59e425ceaad4985e2106651821 100644
--- a/06.label_semantic_roles/README.en.md
+++ b/06.label_semantic_roles/README.en.md
@@ -1,6 +1,6 @@
 # Semantic Role Labeling
 
-The source code of this chapter is live on [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/label_semantic_roles).
+The source code of this chapter is live on [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/06.label_semantic_roles).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
@@ -50,7 +50,7 @@ In this tutorial, our SRL system is built as an end-to-end system via a neural n
 
 ## Model
 
-**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim model long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
+**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim model long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
 
 ### Stacked Recurrent Neural Network
 
@@ -204,7 +204,7 @@ In addition to the data, we provide following resources:
 | predicate_dict | predicate dictionary, total 3162 predicates |
 | emb | a pre-trained word vector lookup table, 32-dimentional |
 
-We trained a language model on the English Wikipedia to get a word vector lookup table used to initialize the SRL model. While training the SRL model, the word vector lookup table is no longer updated. To learn more about the language model and the word vector lookup table, please refer to the tutorial [word vector](https://github.com/PaddlePaddle/book/blob/develop/word2vec/README.md). There are 995,000,000 tokens in the training corpus, and the dictionary size is 4900,000 words. In the CoNLL 2005 training corpus, 5% of the words are not in the 4900,000 words, and we see them all as unknown words, represented by `<unk>`.
+We trained a language model on the English Wikipedia to get a word vector lookup table used to initialize the SRL model. While training the SRL model, the word vector lookup table is no longer updated. To learn more about the language model and the word vector lookup table, please refer to the tutorial [word vector](https://github.com/PaddlePaddle/book/blob/develop/04.word2vec/README.md). There are 995,000,000 tokens in the training corpus, and the dictionary size is 4900,000 words. In the CoNLL 2005 training corpus, 5% of the words are not in the 4900,000 words, and we see them all as unknown words, represented by `<unk>`.
 
 Here we fetch the dictionary, and print its size:
 
diff --git a/06.label_semantic_roles/README.md b/06.label_semantic_roles/README.md
index 309a166d5e598f7793032b2e0bf1dafefcd01854..fe92d852ad3e2a79e5be1d41aaef4ee26df182a1 100644
--- a/06.label_semantic_roles/README.md
+++ b/06.label_semantic_roles/README.md
@@ -1,6 +1,6 @@
 # 语义角色标注
 
-本教程源代码目录在[book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/label_semantic_roles)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/06.label_semantic_roles)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
@@ -40,7 +40,7 @@ $$\mbox{[小明]}_{\mbox{Agent}}\mbox{[昨天]}_{\mbox{Time}}\mbox{[晚上]}_\mb
 
 ## 模型概览
 
-循环神经网络（Recurrent Neural Network）是一种对序列建模的重要模型，在自然语言处理任务中有着广泛地应用。不同于前馈神经网络（Feed-forward Neural Network），RNN能够处理输入之间前后关联的问题。LSTM是RNN的一种重要变种，常用来学习长序列中蕴含的长程依赖关系，我们在[情感分析](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment)一篇中已经介绍过，这一篇中我们依然利用LSTM来解决SRL问题。
+循环神经网络（Recurrent Neural Network）是一种对序列建模的重要模型，在自然语言处理任务中有着广泛地应用。不同于前馈神经网络（Feed-forward Neural Network），RNN能够处理输入之间前后关联的问题。LSTM是RNN的一种重要变种，常用来学习长序列中蕴含的长程依赖关系，我们在[情感分析](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment)一篇中已经介绍过，这一篇中我们依然利用LSTM来解决SRL问题。
 
 ### 栈式循环神经网络（Stacked Recurrent Neural Network）
 
@@ -182,7 +182,7 @@ conll05st-release/
 | predicate_dict | 谓词的词典，共计3162个词 |
 | emb | 一个训练好的词表，32维 |
 
-我们在英文维基百科上训练语言模型得到了一份词向量用来初始化SRL模型。在SRL模型训练过程中，词向量不再被更新。关于语言模型和词向量可以参考[词向量](https://github.com/PaddlePaddle/book/blob/develop/word2vec/README.md) 这篇教程。我们训练语言模型的语料共有995,000,000个token，词典大小控制为4900,000词。CoNLL 2005训练语料中有5%的词不在这4900,000个词中，我们将它们全部看作未登录词，用`<unk>`表示。
+我们在英文维基百科上训练语言模型得到了一份词向量用来初始化SRL模型。在SRL模型训练过程中，词向量不再被更新。关于语言模型和词向量可以参考[词向量](https://github.com/PaddlePaddle/book/blob/develop/04.word2vec/README.md) 这篇教程。我们训练语言模型的语料共有995,000,000个token，词典大小控制为4900,000词。CoNLL 2005训练语料中有5%的词不在这4900,000个词中，我们将它们全部看作未登录词，用`<unk>`表示。
 
 获取词典，打印词典大小：
 
diff --git a/06.label_semantic_roles/index.en.html b/06.label_semantic_roles/index.en.html
index d2edc825c28fce2939e0ab99a06268de870770e5..74b6d477f0664222685f9a5de51fdb9dda9e8f79 100644
--- a/06.label_semantic_roles/index.en.html
+++ b/06.label_semantic_roles/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # Semantic Role Labeling
 
-The source code of this chapter is live on [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/label_semantic_roles).
+The source code of this chapter is live on [book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/06.label_semantic_roles).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
@@ -92,7 +92,7 @@ In this tutorial, our SRL system is built as an end-to-end system via a neural n
 
 ## Model
 
-**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim model long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
+**Recurrent Neural Networks** (*RNN*) are important tools for sequence modeling and have been successfully used in some natural language processing tasks. Unlike feed-forward neural networks, RNNs can model the dependencies between elements of sequences. As a variant of RNNs', LSTMs aim model long-term dependency in long sequences. We have introduced this in [understand_sentiment](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment). In this chapter, we continue to use LSTMs to solve SRL problems.
 
 ### Stacked Recurrent Neural Network
 
@@ -246,7 +246,7 @@ In addition to the data, we provide following resources:
 | predicate_dict | predicate dictionary, total 3162 predicates |
 | emb | a pre-trained word vector lookup table, 32-dimentional |
 
-We trained a language model on the English Wikipedia to get a word vector lookup table used to initialize the SRL model. While training the SRL model, the word vector lookup table is no longer updated. To learn more about the language model and the word vector lookup table, please refer to the tutorial [word vector](https://github.com/PaddlePaddle/book/blob/develop/word2vec/README.md). There are 995,000,000 tokens in the training corpus, and the dictionary size is 4900,000 words. In the CoNLL 2005 training corpus, 5% of the words are not in the 4900,000 words, and we see them all as unknown words, represented by `<unk>`.
+We trained a language model on the English Wikipedia to get a word vector lookup table used to initialize the SRL model. While training the SRL model, the word vector lookup table is no longer updated. To learn more about the language model and the word vector lookup table, please refer to the tutorial [word vector](https://github.com/PaddlePaddle/book/blob/develop/04.word2vec/README.md). There are 995,000,000 tokens in the training corpus, and the dictionary size is 4900,000 words. In the CoNLL 2005 training corpus, 5% of the words are not in the 4900,000 words, and we see them all as unknown words, represented by `<unk>`.
 
 Here we fetch the dictionary, and print its size:
 
diff --git a/06.label_semantic_roles/index.html b/06.label_semantic_roles/index.html
index 3825f742c657c08c83adc446f0d6d5519ce78ede..4e7806580f7b1d83e50fd43b079ce6631c64da16 100644
--- a/06.label_semantic_roles/index.html
+++ b/06.label_semantic_roles/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # 语义角色标注
 
-本教程源代码目录在[book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/label_semantic_roles)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/label_semantic_roles](https://github.com/PaddlePaddle/book/tree/develop/06.label_semantic_roles)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
@@ -82,7 +82,7 @@ $$\mbox{[小明]}_{\mbox{Agent}}\mbox{[昨天]}_{\mbox{Time}}\mbox{[晚上]}_\mb
 
 ## 模型概览
 
-循环神经网络（Recurrent Neural Network）是一种对序列建模的重要模型，在自然语言处理任务中有着广泛地应用。不同于前馈神经网络（Feed-forward Neural Network），RNN能够处理输入之间前后关联的问题。LSTM是RNN的一种重要变种，常用来学习长序列中蕴含的长程依赖关系，我们在[情感分析](https://github.com/PaddlePaddle/book/tree/develop/understand_sentiment)一篇中已经介绍过，这一篇中我们依然利用LSTM来解决SRL问题。
+循环神经网络（Recurrent Neural Network）是一种对序列建模的重要模型，在自然语言处理任务中有着广泛地应用。不同于前馈神经网络（Feed-forward Neural Network），RNN能够处理输入之间前后关联的问题。LSTM是RNN的一种重要变种，常用来学习长序列中蕴含的长程依赖关系，我们在[情感分析](https://github.com/PaddlePaddle/book/tree/develop/05.understand_sentiment)一篇中已经介绍过，这一篇中我们依然利用LSTM来解决SRL问题。
 
 ### 栈式循环神经网络（Stacked Recurrent Neural Network）
 
@@ -224,7 +224,7 @@ conll05st-release/
 | predicate_dict | 谓词的词典，共计3162个词 |
 | emb | 一个训练好的词表，32维 |
 
-我们在英文维基百科上训练语言模型得到了一份词向量用来初始化SRL模型。在SRL模型训练过程中，词向量不再被更新。关于语言模型和词向量可以参考[词向量](https://github.com/PaddlePaddle/book/blob/develop/word2vec/README.md) 这篇教程。我们训练语言模型的语料共有995,000,000个token，词典大小控制为4900,000词。CoNLL 2005训练语料中有5%的词不在这4900,000个词中，我们将它们全部看作未登录词，用`<unk>`表示。
+我们在英文维基百科上训练语言模型得到了一份词向量用来初始化SRL模型。在SRL模型训练过程中，词向量不再被更新。关于语言模型和词向量可以参考[词向量](https://github.com/PaddlePaddle/book/blob/develop/04.word2vec/README.md) 这篇教程。我们训练语言模型的语料共有995,000,000个token，词典大小控制为4900,000词。CoNLL 2005训练语料中有5%的词不在这4900,000个词中，我们将它们全部看作未登录词，用`<unk>`表示。
 
 获取词典，打印词典大小：
 
diff --git a/07.machine_translation/README.en.md b/07.machine_translation/README.en.md
index 5133b8643589d334a1c368d058a8ef1cfcc2dd6c..e306d218552543f00c25cfb9a4965ae5993e5812 100644
--- a/07.machine_translation/README.en.md
+++ b/07.machine_translation/README.en.md
@@ -1,6 +1,6 @@
 # Machine Translation
 
-The source codes is located at [book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/machine_translation). Please refer to the PaddlePaddle [installation tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) if you are a first time user.
+The source codes is located at [book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/07.machine_translation). Please refer to the PaddlePaddle [installation tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) if you are a first time user.
 
 ## Background
 
diff --git a/07.machine_translation/README.md b/07.machine_translation/README.md
index fcc703b7b5a9f379a2c446b231d743a0a5d5c21c..658c1e0506c0c041a8e38d00a74721665177e352 100644
--- a/07.machine_translation/README.md
+++ b/07.machine_translation/README.md
@@ -1,6 +1,6 @@
 # 机器翻译
 
-本教程源代码目录在[book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/machine_translation)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/07.machine_translation)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/07.machine_translation/index.en.html b/07.machine_translation/index.en.html
index 944bf7251e84b7b93c705b3e6dad3ea1478c302c..1df7352b7fc8a1fcb3e83e846843baedfeda77ca 100644
--- a/07.machine_translation/index.en.html
+++ b/07.machine_translation/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # Machine Translation
 
-The source codes is located at [book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/machine_translation). Please refer to the PaddlePaddle [installation tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) if you are a first time user.
+The source codes is located at [book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/07.machine_translation). Please refer to the PaddlePaddle [installation tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) if you are a first time user.
 
 ## Background
 
diff --git a/07.machine_translation/index.html b/07.machine_translation/index.html
index 77cbc6fca36f8ce423c018bad453fd12cdf45bb2..b73212c4911e7d80efa1b107fda3aaa7297671ac 100644
--- a/07.machine_translation/index.html
+++ b/07.machine_translation/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # 机器翻译
 
-本教程源代码目录在[book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/machine_translation)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/07.machine_translation)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/08.recommender_system/README.en.md b/08.recommender_system/README.en.md
index 881fdaee25dac6dbf0874db2551757b025ef2f1d..75bed8af0665a202ce0cfcb165dc8305f42e1722 100644
--- a/08.recommender_system/README.en.md
+++ b/08.recommender_system/README.en.md
@@ -1,6 +1,6 @@
 # Personalized Recommendation
 
-The source code of this tutorial is in [book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/recommender_system).
+The source code of this tutorial is in [book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/08.recommender_system).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
diff --git a/08.recommender_system/README.md b/08.recommender_system/README.md
index 94f072db7233681c53408e44a57dc93483c1cfe7..812042ef9e12304a08ec112e4f4ffbd32da161c3 100644
--- a/08.recommender_system/README.md
+++ b/08.recommender_system/README.md
@@ -1,6 +1,6 @@
 # 个性化推荐
 
-本教程源代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/recommender_system)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/08.recommender_system)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/08.recommender_system/index.en.html b/08.recommender_system/index.en.html
index 922d87f6fd477d5e32284e05162dba1f9ce0e7c6..07ebae273e2282f01779d8ac1e49a0495510ffad 100644
--- a/08.recommender_system/index.en.html
+++ b/08.recommender_system/index.en.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # Personalized Recommendation
 
-The source code of this tutorial is in [book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/recommender_system).
+The source code of this tutorial is in [book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/08.recommender_system).
 
 For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
diff --git a/08.recommender_system/index.html b/08.recommender_system/index.html
index 491b1a6d52e4efa01838cb39531b6f01dd88349a..26514f81b6b97a13259e70083463372e9fbab8c9 100644
--- a/08.recommender_system/index.html
+++ b/08.recommender_system/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # 个性化推荐
 
-本教程源代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/recommender_system)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
+本教程源代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/08.recommender_system)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
 
 ## 背景介绍
 
diff --git a/README.md b/README.md
index 68ffae7e8ec50b11b7ee7c5a9a31215a2ca38544..94b98c6287b44c5c42b889fed5e3d45aa7372e7a 100644
--- a/README.md
+++ b/README.md
@@ -16,17 +16,9 @@
 
 ## 运行这本书
 
-您现在在看的这本书是一本“交互式”电子书 —— 每一章都可以运行在一个
-Jupyter Notebook 里。
-
-我们把 Jupyter、PaddlePaddle、以及各种被依赖的软件都打包进一个 Docker
-image 了。所以您不需要自己来安装各种软件，只需要安装 Docker 即可。如果
-您使用 Windows，可以参
-考[这里](https://www.docker.com/docker-windows)。如果您使用 Mac，可以
-参考[这里](https://www.docker.com/docker-mac)。 对于各种 Linux 发行版，
-请参考https://www.docker.com 。如果您使用 Windows 或者 Mac，可以通过如
-下方法给 Docker 更多内存和CPU资源
-(http://stackoverflow.com/a/39720010/724872)。
+您现在在看的这本书是一本“交互式”电子书 —— 每一章都可以运行在一个Jupyter Notebook里。
+
+我们把Jupyter、PaddlePaddle、以及各种被依赖的软件都打包进一个Docker image了。所以您不需要自己来安装各种软件，只需要安装Docker即可。对于各种Linux发行版，请参考 https://www.docker.com 。如果您使用[Windows](https://www.docker.com/docker-windows)或者[Mac](https://www.docker.com/docker-mac)，可以考虑[给Docker更多内存和CPU资源](http://stackoverflow.com/a/39720010/724872)。
 
 只需要在命令行窗口里运行：
 
@@ -34,11 +26,9 @@ image 了。所以您不需要自己来安装各种软件，只需要安装 Dock
 docker run -d -p 8888:8888 paddlepaddle/book
 ```
 
-这个命令会从 DockerHub.com 下载本书的 Docker image 并且运行之。请在浏
-览器里访问 http://localhost:8888 即可阅读和在线编辑本书。
+会从DockerHub.com下载和运行本书的Docker image。阅读和在线编辑本书请在浏览器里访问 http://localhost:8888 。
 
-如果您访问 DockerHub.com 很慢，可以试试我们的另一个镜像
-docker.paddlepaddle.org：
+如果您访问DockerHub.com很慢，可以试试我们的另一个镜像docker.paddlepaddle.org：
 
 ```bash
 docker run -d -p 8888:8888 docker.paddlepaddle.org/book
@@ -46,14 +36,9 @@ docker run -d -p 8888:8888 docker.paddlepaddle.org/book
 
 ## 贡献内容
 
-您要是能贡献新的章节那就太好了！请发 Pull Requests 把您写的章节加入到
-`/pending` 下面的一个子目录里。当这一章稳定下来，我们一起把您的目录挪
-到根目录。
-
-为了写作、运行、调试，您需要安装 Python 2.x, Go >1.5. 你可以用这
-个[脚本程序](https://github.com/PaddlePaddle/book/blob/develop/.tools/convert-markdown-into-ipynb-and-test.sh)来
-生成 Docker image。
+您要是能贡献新的章节那就太好了！请发Pull Requests把您写的章节加入到`/pending`下面的一个子目录里。当这一章稳定下来，我们一起把您的目录挪到根目录。
 
+为了写作、运行、调试，您需要安装Python 2.x和Go >1.5, 并可以用[脚本程序](https://github.com/PaddlePaddle/book/blob/develop/.tools/convert-markdown-into-ipynb-and-test.sh)来生成新的Docker image。
 
 **Note:** We also provide [English Readme](https://github.com/PaddlePaddle/book/blob/develop/README.en.md) for PaddlePaddle book.
 
diff --git a/index.en.html b/index.en.html
index cbfb7757cee223d335a67ec368ec03a2ea3d1f55..f7ae7db3188cb4dd4a28a424c62d9603e8dc0f32 100644
--- a/index.en.html
+++ b/index.en.html
@@ -11,6 +11,7 @@
 * {
     font-family:"Roboto","Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;
 }
+
 .left-panel {
     background: #E5E6EA;
 }
@@ -64,7 +65,7 @@
 <body>
     <nav class="navbar navbar-toggleable-md navbar-inverse bg-inverse">
         <a class="navbar-brand mr-auto" href="#">
-            <img alt="PaddlePaddle" src="./.theme/PP_w.png">
+            <img alt="PaddlePaddle" src="./.tools/theme/PP_w.png">
         </a>
         <ul class="nav navbar-nav">
           <li class="nav-item">
diff --git a/index.html b/index.html
index 9cc744529ec35fe5dcb356bd1fcb3a2bd354762c..e4bb6268c925a9b527698d2818c446c9814b8b7e 100644
--- a/index.html
+++ b/index.html
@@ -11,6 +11,7 @@
 * {
     font-family:"Roboto","Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;
 }
+
 .left-panel {
     background: #E5E6EA;
 }
@@ -68,7 +69,7 @@
 <body>
     <nav class="navbar navbar-toggleable-md navbar-inverse bg-inverse">
         <a class="navbar-brand mr-auto" href="#">
-            <img alt="PaddlePaddle" src="./.theme/PP_w.png">
+            <img alt="PaddlePaddle" src="./.tools/theme/PP_w.png">
         </a>
         <ul class="nav navbar-nav">
           <li class="nav-item">
diff --git a/pending/gan/index.html b/pending/gan/index.html
index b2d90bde37681294f74d1ac8a051e630c463c838..fb4009c11912a394a88ceafb60c821818a08a109 100644
--- a/pending/gan/index.html
+++ b/pending/gan/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
diff --git a/pending/image_caption/index.html b/pending/image_caption/index.html
index 1cce2a25ce4ffbbabca7791673b824fbfa8e95a3..efbfd8a4e368928d761789c0a475c863ab586e34 100644
--- a/pending/image_caption/index.html
+++ b/pending/image_caption/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
diff --git a/pending/image_detection/index.html b/pending/image_detection/index.html
index 1cce2a25ce4ffbbabca7791673b824fbfa8e95a3..efbfd8a4e368928d761789c0a475c863ab586e34 100644
--- a/pending/image_detection/index.html
+++ b/pending/image_detection/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
diff --git a/pending/image_qa/index.html b/pending/image_qa/index.html
index 1cce2a25ce4ffbbabca7791673b824fbfa8e95a3..efbfd8a4e368928d761789c0a475c863ab586e34 100644
--- a/pending/image_qa/index.html
+++ b/pending/image_qa/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
diff --git a/pending/query_relationship/index.html b/pending/query_relationship/index.html
index 1cce2a25ce4ffbbabca7791673b824fbfa8e95a3..efbfd8a4e368928d761789c0a475c863ab586e34 100644
--- a/pending/query_relationship/index.html
+++ b/pending/query_relationship/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
   </script>
   <link href="http://cdn.bootcss.com/highlight.js/9.9.0/styles/darcula.min.css" rel="stylesheet">
   <script src="http://cdn.bootcss.com/highlight.js/9.9.0/highlight.min.js"></script>
   <link href="http://cdn.bootcss.com/bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" rel="stylesheet">
   <link href="https://cdn.jsdelivr.net/perfect-scrollbar/0.6.14/css/perfect-scrollbar.min.css" rel="stylesheet">
-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
diff --git a/pending/skip_thought/index.html b/pending/skip_thought/index.html
index 1cce2a25ce4ffbbabca7791673b824fbfa8e95a3..efbfd8a4e368928d761789c0a475c863ab586e34 100644
--- a/pending/skip_thought/index.html
+++ b/pending/skip_thought/index.html
@@ -14,13 +14,13 @@
   });
   </script>
   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
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+  <script type="text/javascript" src="../.tools/theme/marked.js">
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-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
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 <style type="text/css" >
 .markdown-body {
diff --git a/pending/speech_recognition/index.html b/pending/speech_recognition/index.html
index 1cce2a25ce4ffbbabca7791673b824fbfa8e95a3..efbfd8a4e368928d761789c0a475c863ab586e34 100644
--- a/pending/speech_recognition/index.html
+++ b/pending/speech_recognition/index.html
@@ -14,13 +14,13 @@
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   <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js" async></script>
-  <script type="text/javascript" src="../.tmpl/marked.js">
+  <script type="text/javascript" src="../.tools/theme/marked.js">
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-  <link href="../.tmpl/github-markdown.css" rel='stylesheet'>
+  <link href="../.tools/theme/github-markdown.css" rel='stylesheet'>
 </head>
 <style type="text/css" >
 .markdown-body {
