Make README.md better

yanshengjia · Dec 15, 2017 · 5e6f75c · 5e6f75c
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diff --git a/24-puzzle/README.md b/24-puzzle/README.md
@@ -1,11 +1,9 @@
-# 24数码问题
+# 24 Puzzle
 
-人工智能课第三次作业
+### Description
 
-### 问题描述
+Solve 24 puzzle problem.
 
-求解24数码问题
+### Approach
 
-### 方法
-
-启发式搜索
+Heuristic Search
diff --git a/24-puzzle/puzzle/data_normal.txt b/24-puzzle/puzzle/data_normal.txt
diff --git a/24-puzzle/puzzle/data_spiral.txt b/24-puzzle/puzzle/data_spiral.txt
diff --git a/24-puzzle/puzzle/puzzle.pro.user b/24-puzzle/puzzle/puzzle.pro.user
@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <!DOCTYPE QtCreatorProject>
-<!-- Written by QtCreator 3.6.1, 2016-05-20T10:18:44. -->
+<!-- Written by QtCreator 3.6.1, 2017-12-15T17:38:16. -->
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@@ -212,7 +212,7 @@
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@@ -231,7 +231,7 @@
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diff --git a/genetic-algorithm-for-functional-maximum-problem/README.md b/genetic-algorithm-for-functional-maximum-problem/README.md
@@ -1,16 +1,14 @@
-# 基于遗传算法的优化问题求解
+# Genetic Algorithm for Functional Maximum Problem
 
-人工智能课第二次作业
+### Description
 
-### 问题
+Find out the maximum value of the function *f(x)=x+10sin(5x)+7cos(4x)* in the range [0, 9].
 
-求解函数 f(x)=x+10sin(5x)+7cos(4x) 在区间[0,9]的极大值
+### Approach
 
-### 方法
+Genetic Algorithm
 
-遗传算法
-
-### 答案
+### Ground Truth
 
 24.8553627239529
 x = 7.857
diff --git a/million-queens/README.md b/million-queens/README.md
@@ -1,13 +1,9 @@
-# 百万皇后问题
+# Million Queens
 
-人工智能课第四次作业
+### Description
 
-### 问题描述
+Solve million queens problem.
 
-求解百万皇后问题
+### Approach
 
-### 方法
-
-采用QS4算法，求解3000000皇后一般能在3秒之内得到一个可行解，若未搜索到随机重启。
-
-算法来自 Sosic, R.; Gu, J. 3,000,000 Queens in less than one minute. ACM SIGART Bulletin, 1991, 2(2).
+QS4 algorithm [Sosic, R.; Gu, J. 3,000,000 Queens in less than one minute. ACM SIGART Bulletin, 1991, 2(2).]
diff --git a/neural-networks-for-face-recognition/README.md b/neural-networks-for-face-recognition/README.md
@@ -1,17 +1,26 @@
-# README
+# Neural Networks for Face Recognition
 
-这是人工智能研讨课第一次作业的代码和源码。
+## Description
 
 基于人工神经网络和反向传播算法来识别人脸的特征。
 
 算法和代码参考了《机器学习/ (美) 米歇尔(Mitchell, T. M.)著; 曾华军等译. ——北京: 机械工业出版社, 2003. 1》第四章的内容。
 
-数据集来自
+## Dataset
+
 [Neural Networks for Face Recognition](http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/faces.html)
 
-数据集中包含20个子文件夹，每个文件夹包含1个人，一共20人。图片的名称包含了这张图片的userid, sunglass, pose, expression, scale的信息。
+数据集中包含20个子文件夹，每个文件夹包含1个人，一共20人。图片的名称包含了这张图片的 userid, sunglass, pose, expression, scale 的信息。
+
+## Usage
+
+```shell
+$ cd project-path
+$ make
+$ ./facetrain
+```
 
-需要修改 **.list**文件中图片路径，以让程序能正确读取输入数据。
+需要修改  **.list **文件中图片路径，以让程序能正确读取输入数据。
 
 输入一张人脸图片，我们的ANN主要可以做5件事：
 
@@ -21,10 +30,6 @@
 * pose-recognizer 识别他脸的朝向
 * expression-recognizer 识别他的表情
 
-工程需要编译才能运行，cd进工程所在路径，输入make，会生成可执行文件facetrain，运行facetrain来执行上述功能。
-
 当然，facetrain的功能也不止于此，它还可以输出分类失败的图片列表以及将隐藏层权值可视化等等。
 
 facetrain的具体用法请参考 doc/ysjai01.pdf，同时这份文档也详细介绍我的实验过程、实验结果和心得体会。
-
-：）