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Title: GitHub - CodeWarriorProDevStar/MachineLearning_Python: 机器学习算法python实现

Open Graph Title: GitHub - CodeWarriorProDevStar/MachineLearning_Python: 机器学习算法python实现

X Title: GitHub - CodeWarriorProDevStar/MachineLearning_Python: 机器学习算法python实现

Description: 机器学习算法python实现. Contribute to CodeWarriorProDevStar/MachineLearning_Python development by creating an account on GitHub.

Open Graph Description: 机器学习算法python实现. Contribute to CodeWarriorProDevStar/MachineLearning_Python development by creating an account on GitHub.

X Description: 机器学习算法python实现. Contribute to CodeWarriorProDevStar/MachineLearning_Python development by creating an account on GitHub.

Opengraph URL: https://github.com/CodeWarriorProDevStar/MachineLearning_Python

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MIT license	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#机器学习算法python实现
	https://github.com/lawlite19/MachineLearning_Python/blob/master/LICENSE
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#目录
机器学习算法Python实现	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95python%E5%AE%9E%E7%8E%B0
一、线性回归	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E4%B8%80%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92
1、代价函数	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E4%BB%A3%E4%BB%B7%E5%87%BD%E6%95%B0
2、梯度下降算法	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D%E7%AE%97%E6%B3%95
3、均值归一化	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3%E5%9D%87%E5%80%BC%E5%BD%92%E4%B8%80%E5%8C%96
4、最终运行结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4%E6%9C%80%E7%BB%88%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C
5、使用scikit-learn库中的线性模型实现	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5%E4%BD%BF%E7%94%A8scikit-learn%E5%BA%93%E4%B8%AD%E7%9A%84%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B%E5%AE%9E%E7%8E%B0
二、逻辑回归	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E4%BA%8C%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92
1、代价函数	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E4%BB%A3%E4%BB%B7%E5%87%BD%E6%95%B0
2、梯度	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2%E6%A2%AF%E5%BA%A6
3、正则化	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3%E6%AD%A3%E5%88%99%E5%8C%96
4、S型函数（即）	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4s%E5%9E%8B%E5%87%BD%E6%95%B0%E5%8D%B3
5、映射为多项式	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5%E6%98%A0%E5%B0%84%E4%B8%BA%E5%A4%9A%E9%A1%B9%E5%BC%8F
6、使用的优化方法	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6%E4%BD%BF%E7%94%A8scipy%E7%9A%84%E4%BC%98%E5%8C%96%E6%96%B9%E6%B3%95
7、运行结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C
8、使用scikit-learn库中的逻辑回归模型实现	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#8%E4%BD%BF%E7%94%A8scikit-learn%E5%BA%93%E4%B8%AD%E7%9A%84%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92%E6%A8%A1%E5%9E%8B%E5%AE%9E%E7%8E%B0
逻辑回归_手写数字识别_OneVsAll	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92_%E6%89%8B%E5%86%99%E6%95%B0%E5%AD%97%E8%AF%86%E5%88%AB_onevsall
1、随机显示100个数字	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E9%9A%8F%E6%9C%BA%E6%98%BE%E7%A4%BA100%E4%B8%AA%E6%95%B0%E5%AD%97
2、OneVsAll	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2onevsall
3、手写数字识别	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3%E6%89%8B%E5%86%99%E6%95%B0%E5%AD%97%E8%AF%86%E5%88%AB
4、预测	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4%E9%A2%84%E6%B5%8B
5、运行结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C
6、使用scikit-learn库中的逻辑回归模型实现	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6%E4%BD%BF%E7%94%A8scikit-learn%E5%BA%93%E4%B8%AD%E7%9A%84%E9%80%BB%E8%BE%91%E5%9B%9E%E5%BD%92%E6%A8%A1%E5%9E%8B%E5%AE%9E%E7%8E%B0
三、BP神经网络	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E4%B8%89bp%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C
1、神经网络model	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9Cmodel
2、代价函数	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2%E4%BB%A3%E4%BB%B7%E5%87%BD%E6%95%B0
3、正则化	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3%E6%AD%A3%E5%88%99%E5%8C%96
4、反向传播BP	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%ADbp
5、BP可以求梯度的原因	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5bp%E5%8F%AF%E4%BB%A5%E6%B1%82%E6%A2%AF%E5%BA%A6%E7%9A%84%E5%8E%9F%E5%9B%A0
6、梯度检查	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6%E6%A2%AF%E5%BA%A6%E6%A3%80%E6%9F%A5
7、权重的随机初始化	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7%E6%9D%83%E9%87%8D%E7%9A%84%E9%9A%8F%E6%9C%BA%E5%88%9D%E5%A7%8B%E5%8C%96
8、预测	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#8%E9%A2%84%E6%B5%8B
9、输出结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#9%E8%BE%93%E5%87%BA%E7%BB%93%E6%9E%9C
四、SVM支持向量机	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E5%9B%9Bsvm%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA
1、代价函数	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E4%BB%A3%E4%BB%B7%E5%87%BD%E6%95%B0
2、Large Margin	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2large-margin
3、SVM Kernel（核函数）	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3svm-kernel%E6%A0%B8%E5%87%BD%E6%95%B0
4、使用中的模型代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4%E4%BD%BF%E7%94%A8scikit-learn%E4%B8%AD%E7%9A%84svm%E6%A8%A1%E5%9E%8B%E4%BB%A3%E7%A0%81
5、运行结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C
五、K-Means聚类算法	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E4%BA%94k-means%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95
1、聚类过程	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E8%81%9A%E7%B1%BB%E8%BF%87%E7%A8%8B
2、目标函数	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2%E7%9B%AE%E6%A0%87%E5%87%BD%E6%95%B0
3、聚类中心的选择	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3%E8%81%9A%E7%B1%BB%E4%B8%AD%E5%BF%83%E7%9A%84%E9%80%89%E6%8B%A9
4、聚类个数K的选择	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4%E8%81%9A%E7%B1%BB%E4%B8%AA%E6%95%B0k%E7%9A%84%E9%80%89%E6%8B%A9
5、应用——图片压缩	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5%E5%BA%94%E7%94%A8%E5%9B%BE%E7%89%87%E5%8E%8B%E7%BC%A9
6、使用scikit-learn库中的线性模型实现聚类	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6%E4%BD%BF%E7%94%A8scikit-learn%E5%BA%93%E4%B8%AD%E7%9A%84%E7%BA%BF%E6%80%A7%E6%A8%A1%E5%9E%8B%E5%AE%9E%E7%8E%B0%E8%81%9A%E7%B1%BB
7、运行结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C
六、PCA主成分分析（降维）	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E5%85%ADpca%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90%E9%99%8D%E7%BB%B4
1、用处	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E7%94%A8%E5%A4%84
2、2D-->1D，nD-->kD	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#22d--1dnd--kd
3、主成分分析PCA与线性回归的区别	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3%E4%B8%BB%E6%88%90%E5%88%86%E5%88%86%E6%9E%90pca%E4%B8%8E%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E7%9A%84%E5%8C%BA%E5%88%AB
4、PCA降维过程	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4pca%E9%99%8D%E7%BB%B4%E8%BF%87%E7%A8%8B
5、数据恢复	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5%E6%95%B0%E6%8D%AE%E6%81%A2%E5%A4%8D
6、主成分个数的选择（即要降的维度）	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6%E4%B8%BB%E6%88%90%E5%88%86%E4%B8%AA%E6%95%B0%E7%9A%84%E9%80%89%E6%8B%A9%E5%8D%B3%E8%A6%81%E9%99%8D%E7%9A%84%E7%BB%B4%E5%BA%A6
7、使用建议	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7%E4%BD%BF%E7%94%A8%E5%BB%BA%E8%AE%AE
8、运行结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#8%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C
9、使用scikit-learn库中的PCA实现降维	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#9%E4%BD%BF%E7%94%A8scikit-learn%E5%BA%93%E4%B8%AD%E7%9A%84pca%E5%AE%9E%E7%8E%B0%E9%99%8D%E7%BB%B4
七、异常检测 Anomaly Detection	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#%E4%B8%83%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B-anomaly-detection
1、高斯分布（正态分布）	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1%E9%AB%98%E6%96%AF%E5%88%86%E5%B8%83%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83gaussian-distribution
2、异常检测算法	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B%E7%AE%97%E6%B3%95
3、评价的好坏，以及的选取	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3%E8%AF%84%E4%BB%B7px%E7%9A%84%E5%A5%BD%E5%9D%8F%E4%BB%A5%E5%8F%8A%CE%B5%E7%9A%84%E9%80%89%E5%8F%96
4、选择使用什么样的feature（单元高斯分布）	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4%E9%80%89%E6%8B%A9%E4%BD%BF%E7%94%A8%E4%BB%80%E4%B9%88%E6%A0%B7%E7%9A%84feature%E5%8D%95%E5%85%83%E9%AB%98%E6%96%AF%E5%88%86%E5%B8%83
5、多元高斯分布	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5%E5%A4%9A%E5%85%83%E9%AB%98%E6%96%AF%E5%88%86%E5%B8%83
6、单元和多元高斯分布特点	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6%E5%8D%95%E5%85%83%E5%92%8C%E5%A4%9A%E5%85%83%E9%AB%98%E6%96%AF%E5%88%86%E5%B8%83%E7%89%B9%E7%82%B9
7、程序运行结果	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7%E7%A8%8B%E5%BA%8F%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C
线性回归	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LinearRegression
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#一线性回归
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LinearRegression/LinearRegression.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1代价函数
	https://camo.githubusercontent.com/4c31a90ef7d5a94b36a26f6ca594cbd95a4d9893b419cc30d3b86092d2ba3e04/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d4a2532382535437468657461253230253239253230253344253230253543667261632537423125374425374225374232253742253543746578742537426d25374425374425374425374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253742253742253238253742685f253543746865746125323025374425323825374278253545253742253238692532392537442537442532392532302d253230253742792535452537422532386925323925374425374425323925374425354532253744253744253230
	https://camo.githubusercontent.com/0eb6fa9e71155c6608019f7c11202c1aa1bc9f4a903915992451595c5fb81844/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742685f25354374686574612532302537442532387825323925323025334425323025374225354374686574612532305f3025374425323025324225323025374225354374686574612532305f31253744253742785f3125374425323025324225323025374225354374686574612532305f32253744253742785f322537442532302532422532302e2e2e
	https://camo.githubusercontent.com/57f1522524cbdab28ea2d7b064b37ab6dfd3880121571dc150a8526ecfdf8fba/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742253742253742253238253742685f253543746865746125323025374425323825374278253545253742253238692532392537442537442532392532302d253230253742792535452537422532386925323925374425374425323925374425354532253744253744
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2梯度下降算法
	https://camo.githubusercontent.com/b02007afc938ffd408a4a8bee48428c7bc0b32b179f48e91dc0344f99d26d333/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d25374225374225354374686574612532305f6a253744253744
	https://camo.githubusercontent.com/b4c704104dbc22b23f487f159ef20f29a99de6214668e0f428609a7901a4af9c/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253543667261632537422537422535437061727469616c2532304a25323825354374686574612532302532392537442537442537422537422535437061727469616c25323025374225354374686574612532305f6a25374425374425374425323025334425323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253542253238253742685f253543746865746125323025374425323825374278253545253742253238692532392537442537442532392532302d2532302537427925354525374225323869253239253744253744253239785f6a25354525374225323869253239253744253544253744253230
	https://camo.githubusercontent.com/d8e21e3b492667ba66ef67943cc24bc32a4691399e333c96165f905ff2345e9a/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d25374225354374686574612532305f6a25374425323025334425323025374225354374686574612532305f6a2537442532302d253230253543616c70686125323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253542253238253742685f253543746865746125323025374425323825374278253545253742253238692532392537442537442532392532302d2532302537427925354525374225323869253239253744253744253239785f6a25354525374225323869253239253744253544253744253230
	https://camo.githubusercontent.com/e017c2de88067341fb5630fc1163322cee3bfa4d2e10666b503bd0148898c917/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253543616c706861253230
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3均值归一化
	https://camo.githubusercontent.com/97cd58e6c475b3b6e74f9d8e3dbd5c876efb14c656a583c30bc776356db83359/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742785f6925374425323025334425323025354366726163253742253742253742785f692537442532302d2532302537422535436d752532305f69253744253744253744253742253742253742735f69253744253744253744
	https://camo.githubusercontent.com/9cf2ac7beeb1e05ea3e09aadff2216d363c4d6aba66ff3cc2c8f7f117c24f3ba/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d2537422537422535436d752532305f69253744253744
	https://camo.githubusercontent.com/c9c7eb2a377c0a4616a6d130895731a14105f28c1e1a10d83840d7a758d4634a/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742253742735f69253744253744
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4最终运行结果
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LinearRegression_01.png
使用scikit-learn库中的线性模型实现	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LinearRegression/LinearRegression_scikit-learn.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5使用scikit-learn库中的线性模型实现
逻辑回归	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LogisticRegression
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#二逻辑回归
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1代价函数-1
	https://camo.githubusercontent.com/3ba25d174662bc6f4f490e41436b42a6ff383430b3267ce0d5cc1905e6c86bac/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f2535436c617267652532302535436c656674253543253742253230253543626567696e25374267617468657265642537442532304a253238253543746865746125323025323925323025334425323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253543636f7325323074253238253742685f2535437468657461253230253744253238253742782535452537422532386925323925374425374425323925324325374279253545253742253238692532392537442537442532392537442532302535436866696c6c253230253543253543253230253543636f7325323074253238253742685f253543746865746125323025374425323878253239253243792532392532302533442532302535436c656674253543253742253230253742253543626567696e2537426172726179253744253742632537442532302537422532302d2532302535436c6f67253230253238253742685f2535437468657461253230253744253238782532392532392537442532302535432535432532302537422532302d2532302535436c6f67253230253238312532302d253230253742685f253543746865746125323025374425323878253239253239253744253230253543656e642537426172726179253744253230253543626567696e25374261727261792537442537426325374425323025374279253230253344253230312537442532302535432535432532302537427925323025334425323030253744253230253543656e64253742617272617925374425323025374425323025354372696768742e2532302535436866696c6c253230253543253543253230253543656e64253742676174686572656425374425323025354372696768742e
	https://camo.githubusercontent.com/04906df7515891bfc89814d3d5c03430ade17373c1c460c86d9665ead608dfd9/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d4a25323825354374686574612532302532392532302533442532302532302d25323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d25323025374225354225374279253545253742253238692532392537442537442535436c6f67253230253238253742685f25354374686574612532302537442532382537427825354525374225323869253239253744253744253239253230253242253230253238312532302d25323025374425323025374279253545253742253238692532392537442537442532392535436c6f67253230253238312532302d253230253742685f25354374686574612532302537442532382537427825354525374225323869253239253744253744253239253544
	https://camo.githubusercontent.com/2ce4f65e4f7a92390367fe2783a322cfe14cc4ae4617ed4ffd58d5647e071594/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742685f253543746865746125323025374425323878253239253230253344253230253543667261632537423125374425374225374231253230253242253230253742652535452537422532302d25323078253744253744253744253744
	https://camo.githubusercontent.com/17d4d18b2575cfc08f228c765aa6a0e5b39f79ee34a3df1c5cc58e13a7d15bf5/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d2537422532302d2532302535436c6f67253230253238253742685f253543746865746125323025374425323878253239253239253744
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_01.png
	https://camo.githubusercontent.com/553e837c6635683f520d7e824445d1daae62ad56e3cf8a832253d92a0522fba7/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742253742685f253543746865746125323025374425323878253239253744
	https://camo.githubusercontent.com/553e837c6635683f520d7e824445d1daae62ad56e3cf8a832253d92a0522fba7/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742253742685f253543746865746125323025374425323878253239253744
	https://camo.githubusercontent.com/c537da7aea9f66b31a8c479ffa964f051cd683b4115716d84937037a41e76540/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d2537422532302d2532302535436c6f67253230253238312532302d253230253742685f253543746865746125323025374425323878253239253239253744
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_02.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2梯度
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_03.jpg
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3正则化
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4s型函数即
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5映射为多项式
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6使用scipy的优化方法
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7运行结果
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_04.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_05.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_06.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_07.png
使用scikit-learn库中的逻辑回归模型实现	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_scikit-learn.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#8使用scikit-learn库中的逻辑回归模型实现
逻辑回归_手写数字识别_OneVsAll	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LogisticRegression
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#逻辑回归_手写数字识别_onevsall
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_OneVsAll.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1随机显示100个数字
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_08.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_09.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2onevsall
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_11.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3手写数字识别
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_10.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4预测
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5运行结果
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_12.png
使用scikit-learn库中的逻辑回归模型实现	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_OneVsAll_scikit-learn.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6使用scikit-learn库中的逻辑回归模型实现
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/LogisticRegression_13.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#三bp神经网络
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/NeuralNetwok/NeuralNetwork.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1神经网络model
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2代价函数
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3正则化-1
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4反向传播bp
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4使用scikit-learn中的svm模型代码
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/SVM/SVM_scikit-learn.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5运行结果-1
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/SVM_09.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/SVM_10.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#五k-means聚类算法
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/K-Means/K-Menas.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1聚类过程
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/K-Means_01.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/K-Means_02.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/K-Means_03.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#2目标函数
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	https://camo.githubusercontent.com/bab62cd41e2de99f132796d387b7275c4d95df955e57f1435b43d6a110dd0a51/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742755f69253744
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3聚类中心的选择
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4聚类个数k的选择
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/K-Means_04.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5应用图片压缩
使用scikit-learn库中的线性模型实现聚类	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/K-Means/K-Means_scikit-learn.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6使用scikit-learn库中的线性模型实现聚类
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7运行结果-1
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/K-Means_05.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/K-Means_06.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#六pca主成分分析降维
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/PCA/PCA.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1用处
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#22d--1dnd--kd
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_01.png
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3主成分分析pca与线性回归的区别
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4pca降维过程
	https://camo.githubusercontent.com/24c2003379ff16ddca7f830b60e361e5b9fd0c6e90c4b19eae9fbc078845be2c/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253234253234253742253543726d253742782537442537445f6a25354525374225323869253239253744253230253344253230253742253742253742253543726d253742782537442537445f6a253545253742253238692532392537442532302d253230253742755f6a2537442537442532302535436f766572253230253742253742735f6a253744253744253744253234253234
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_02.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_03.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#5数据恢复
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#6主成分个数的选择即要降的维度
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7使用建议
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#8运行结果
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_04.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_05.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_06.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_07.png
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/images/PCA_08.png
使用scikit-learn库中的PCA实现降维	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/PCA/PCA.py_scikit-learn.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#9使用scikit-learn库中的pca实现降维
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#七异常检测-anomaly-detection
全部代码	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python/blob/master/AnomalyDetection/AnomalyDetection.py
	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#1高斯分布正态分布gaussian-distribution
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#3评价px的好坏以及ε的选取
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#4选择使用什么样的feature单元高斯分布
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	https://patch-diff.githubusercontent.com/CodeWarriorProDevStar/MachineLearning_Python#7程序运行结果
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