update

Author: Weichen Shen

README.md

@@ -13,7 +13,7 @@
 [![Build Status](https://travis-ci.org/shenweichen/DeepCTR.svg?branch=master)](https://travis-ci.org/shenweichen/DeepCTR)
 [![Coverage Status](https://coveralls.io/repos/github/shenweichen/DeepCTR/badge.svg?branch=master)](https://coveralls.io/github/shenweichen/DeepCTR?branch=master)
 [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/app/wcshen1994/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade)
-[![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](#Disscussion-Group)
+[![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./#DisscussionGroup)
 [![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE)
 <!-- [![Gitter](https://badges.gitter.im/DeepCTR/community.svg)](https://gitter.im/DeepCTR/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) -->
 
@@ -46,7 +46,7 @@ Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Star
 |                FiBiNET                 | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)   |
 
 
-## Disscussion Group  
+## DisscussionGroup  
 
 Please follow our wechat to join group:  
 - 公众号：**浅梦的学习笔记**  

docs/pics/deepctrbot.jpeg

@@ -24,7 +24,7 @@ Welcome to DeepCTR's documentation!
 .. _Issues: https://github.com/shenweichen/deepctr/issues
 
 .. |Chat| image:: https://img.shields.io/badge/chat-wechat-brightgreen?style=flat
-.. _Chat: ./#Disscussion-Group
+.. _Chat: ./#DisscussionGroup
 
 DeepCTR is a **Easy-to-use** , **Modular** and **Extendible** package of deep-learning based CTR models along with lots of core components layer  which can be used to build your own custom model easily.It is compatible with **tensorflow 1.4+ and 2.0+**.You can use any complex model with ``model.fit()`` and ``model.predict()``.
 
@@ -40,12 +40,12 @@ News
 
 08/02/2019 : Now DeepCTR is compatible with tensorflow `1.14` and `2.0.0`. `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.6.0>`_
 
-Disscussion Group
+DisscussionGroup
 -----------------------
 
 - 公众号：**浅梦的学习笔记**  微信ID: **deepctrbot**
 
-image:: ../pics/weichennote.png
+.. image:: ../pics/weichennote.png
 
 .. toctree::
    :maxdepth: 2