update README

Author: orion-orion

README.md

@@ -6,26 +6,39 @@
  * @LastEditors: ZhangHongYu
  * @LastEditTime: 2022-07-02 15:15:19
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-# Distributed Algorithm using PySpark
-[![Open Source Love](https://badges.frapsoft.com/os/v2/open-source.svg?v=103)](https://github.com/orion-orion/Distributed-Algorithm-PySpark)
-[![](https://img.shields.io/github/license/orion-orion/Distributed-Algorithm-PySpark)](https://github.com/orion-orion/Distributed-Algorithm-PySpark/blob/master/LICENSE)
+<p align="center">
+<img src="pic/DistributedML-cover.png" width="300" height="400">
+</p>
 
-## 1 Introduction
-Implementations of distributed algorithms using PySpark, including graph computaion, matrix computation, randomized algorithm, optimization and machine learning. In particular, We referenced the meterial presented in [《CME 323: Distributed Algorithms and Optimization》](https://stanford.edu/~rezab/classes/cme323/S17/).
-## 2 Contents
+<div align="center">
 
-- Graph Computation
+# 分布式机器学习
+📚 *如果船长的最高目标是保住他的船，那么他只能永远待在港口。*
+
+[![Open Source Love](https://badges.frapsoft.com/os/v2/open-source.svg?v=103)](https://github.com/orion-orion/Distributed-Algorithm-PySpark)[![](https://img.shields.io/github/license/orion-orion/Distributed-Algorithm-PySpark)](https://github.com/orion-orion/Distributed-Algorithm-PySpark/blob/master/LICENSE)[![](https://img.shields.io/github/stars/orion-orion/Distributed-ML-PySpark?style=social)](https://github.com/orion-orion/Distributed-ML-PySpark) 
+<br/>
+[![](https://img.shields.io/github/directory-file-count/orion-orion/Distributed-ML-PySpark)](https://github.com/orion-orion/Distributed-ML-PySpark) [![](https://img.shields.io/github/languages/code-size/orion-orion/Distributed-ML-PySpark)](https://github.com/orion-orion/Distributed-ML-PySpark) 
+</div>
+
+
+## 1 简介
+本项目为经典分布式机器学习算法的的PySpark/Pytorch实现, 主要参考了刘铁岩的《分布式机器学习》和[《CME 323: Distributed Algorithms and Optimization》](https://stanford.edu/~rezab/classes/cme323/S17/)课程。主要内容包括分布式实现的图计算、矩阵计算、随机算法、数值优化和机器学习算法。
+
+## 2 目录
+
+- 图计算
     - PageRank  [[explanation]](https://www.cnblogs.com/orion-orion/p/16340839.html)
     - Transitive Closure
-- Machine Learning
+- 机器学习
     - K-means
     - Logistic Regression  [[explanation]](https://www.cnblogs.com/orion-orion/p/16318810.html)
-- Matrix Computation
+- 矩阵计算
     - Matrix Decomposition
-- Optimization
+- 数值优化
     - Synchronous Stochastic Gradient Descent (SSGD) [[explanation]](https://www.cnblogs.com/orion-orion/p/16413182.html) [[paper]](https://proceedings.neurips.cc/paper/2010/file/abea47ba24142ed16b7d8fbf2c740e0d-Paper.pdf)
+      -  SSGD in Pytorch [[explanation]](https://www.cnblogs.com/orion-orion/p/16413182.html) [[paper]](https://proceedings.neurips.cc/paper/2010/file/abea47ba24142ed16b7d8fbf2c740e0d-Paper.pdf)
     - Model Average (MA) [[explanation]](https://www.cnblogs.com/orion-orion/p/16426982.html) [[paper]](https://aclanthology.org/N10-1069.pdf)
     - Block-wise Model Update Filtering (BMUF) [[explanation]](https://www.cnblogs.com/orion-orion/p/16426982.html) [[paper]](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/0005880.pdf)
     - Elastic Averaging Stochastic Gradient Descent  (EASGD) [[explanation]](https://www.cnblogs.com/orion-orion/p/16426982.html) [[paper]](https://proceedings.neurips.cc/paper/2015/file/d18f655c3fce66ca401d5f38b48c89af-Paper.pdf)
-- Randomized Algorithm
+- 随机算法
     - Monte Carlo Method