!891 fix lite quick start.

Merge pull request !891 from CHERY_Zzz/master

lite/tutorials/source_en/quick_start/quick_start.md

-# Quick Start 
+# Implementing an Image Classification Application
 
 <!-- TOC -->
 
-- [Quick Start ](#quick-start)
+- [Implementing an Image Classification Application](#implementing-an-image-classification-application)
     - [Overview](#overview)
     - [Selecting a Model](#selecting-a-model)
     - [Converting a Model](#converting-a-model)
@@ -133,7 +133,7 @@ app
 
 ### Configuring MindSpore Lite Dependencies
 
-When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://www.mindspore.cn/lite/tutorial/en/master/compile.html) to generate the `libmindspore-lite.so` library file.
+When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://www.mindspore.cn/lite/tutorial/en/master/build.html) to generate the `libmindspore-lite.so` library file.
 
 In Android Studio, place the compiled `libmindspore-lite.so` library file (which can contain multiple compatible architectures) in the `app/libs/ARM64-V8a` (Arm64) or `app/libs/armeabi-v7a` (Arm32) directory of the application project. In the `build.gradle` file of the application, configure the compilation support of CMake, `arm64-v8a`, and `armeabi-v7a`.　　
 

lite/tutorials/source_zh_cn/quick_start/quick_start.md

-# 快速入门
+# 实现一个图像分类应用
 
 <!-- TOC -->
 
-- [快速入门](#快速入门)
+- [实现一个图像分类应用](#实现一个图像分类应用)
     - [概述](#概述)
     - [选择模型](#选择模型)
     - [转换模型](#转换模型)
@@ -33,7 +33,7 @@
 ## 选择模型
 
 MindSpore团队提供了一系列预置终端模型，你可以在应用程序中使用这些预置的终端模型。  
-MindSpore Model Zoo中图像分类模型可[在此下载]((https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms))。
+MindSpore Model Zoo中图像分类模型可[在此下载](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms)。
 同时，你也可以使用预置模型做迁移学习，以实现自己的图像分类任务。
 
 ## 转换模型
@@ -134,7 +134,7 @@ app
 
 ### 配置MindSpore Lite依赖项
 
-Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/tutorial/zh-CN/master/compile.html)生成`libmindspore-lite.so`库文件。
+Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/tutorial/zh-CN/master/build.html)生成`libmindspore-lite.so`库文件。
 
 本示例中，bulid过程由download.gradle文件配置自动下载`libmindspore-lite.so`以及OpenCV的`libopencv_java4.so`库文件，并放置在`app/libs/arm64-v8a`目录下。