Sample chapter roundup for now

02.recognize_digits/README.en.md

 # Recognize Digits
 
-The source code for this tutorial is live at [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [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 live at [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). To get started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
 ## Introduction
-When one learns to program, 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 recognize hand-written digits on the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic 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 $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.
+Handwriting recognition is a classic image classification problem. In particular, recognizing hand-written digits on [MNIST](http://yann.lecun.com/exdb/mnist/) is the deep learning equivalence to a 'Hello World!' program.
+
+### MNIST
+
+MNIST is a complete dataset containing images of handwritten digits and their corresponding labels. Each of the input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$ (Fig. 1). All images are normalized, meaning that they are both scaled and centered.
 
 <p align="center">
 <img src="image/mnist_example_image.png" width="400"><br/>

02.recognize_digits/index.en.html

@@ -42,10 +42,14 @@
 <div id="markdown" style='display:none'>
 # Recognize Digits
 
-The source code for this tutorial is live at [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [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 live at [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). To get started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
 
 ## Introduction
-When one learns to program, 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 recognize hand-written digits on the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic 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 $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.
+Handwriting recognition is a classic image classification problem. In particular, recognizing hand-written digits on [MNIST](http://yann.lecun.com/exdb/mnist/) is the deep learning equivalence to a 'Hello World!' program.
+
+### MNIST
+
+MNIST is a complete dataset containing images of handwritten digits and their corresponding labels. Each of the input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$ (Fig. 1). All images are normalized, meaning that they are both scaled and centered.
 
 <p align="center">
 <img src="image/mnist_example_image.png" width="400"><br/>

07.machine_translation/README.en.md

 # Machine Translation
 
-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.
+The source code of this tutorial is live at [book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/07.machine_translation). Please refer to the [installation tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) for getting started with Paddle.
 
 ## Background
 
@@ -448,7 +448,7 @@ cd pretrained
 
 ### BLEU Evaluation
 
-BLEU (Bilingual Evaluation understudy) is a metric widely used for automatic machine translation proposed by IBM Watson Research Center in 2002\[[5](#References)\]. The closer the translation produced by a machine is to the translation produced by a human expert, the better the performance of the translation system.
+Bilingual Evaluation understudy (BLEU) is a metric widely used for automatic machine translation proposed by IBM Watson Research Center in 2002\[[5](#References)\]. The closer the translation produced by a machine is to the translation produced by a human expert, the better the performance of the translation system.
 To measure the closeness between machine translation and human translation, sentence precision is used. It compares the number of matched n-grams. More matches will lead to higher BLEU scores.
 
 [Moses](http://www.statmt.org/moses/) is an open-source machine translation system, we used [multi-bleu.perl](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl) for BLEU evaluation. Run the following command for downloading:

07.machine_translation/index.en.html

@@ -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/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.
+The source code of this tutorial is live at [book/machine_translation](https://github.com/PaddlePaddle/book/tree/develop/07.machine_translation). Please refer to the [installation tutorial](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst) for getting started with Paddle.
 
 ## Background
 
@@ -490,7 +490,7 @@ cd pretrained
 
 ### BLEU Evaluation
 
-BLEU (Bilingual Evaluation understudy) is a metric widely used for automatic machine translation proposed by IBM Watson Research Center in 2002\[[5](#References)\]. The closer the translation produced by a machine is to the translation produced by a human expert, the better the performance of the translation system.
+Bilingual Evaluation understudy (BLEU) is a metric widely used for automatic machine translation proposed by IBM Watson Research Center in 2002\[[5](#References)\]. The closer the translation produced by a machine is to the translation produced by a human expert, the better the performance of the translation system.
 To measure the closeness between machine translation and human translation, sentence precision is used. It compares the number of matched n-grams. More matches will lead to higher BLEU scores.
 
 [Moses](http://www.statmt.org/moses/) is an open-source machine translation system, we used [multi-bleu.perl](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/multi-bleu.perl) for BLEU evaluation. Run the following command for downloading: