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

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

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

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

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

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

Opengraph URL: https://github.com/expectation-maximization/MachineLearning_Python

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https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#目录
机器学习算法Python实现https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/MachineLearning_Python#1-%E4%BB%A3%E4%BB%B7%E5%87%BD%E6%95%B0
2、梯度https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2-%E6%A2%AF%E5%BA%A6
3、正则化https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3-%E6%AD%A3%E5%88%99%E5%8C%96
4、S型函数（即）https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4-s%E5%9E%8B%E5%87%BD%E6%95%B0%E5%8D%B3
5、映射为多项式https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#6-%E4%BD%BF%E7%94%A8%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/expectation-maximization/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/expectation-maximization/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_OneVsAllhttps://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/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、OneVsAllhttps://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2-onevsall
3、手写数字识别https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#4-%E9%A2%84%E6%B5%8B
5、运行结果https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/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/expectation-maximization/MachineLearning_Python#%E4%B8%89-bp%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C
1、神经网络modelhttps://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#2-%E4%BB%A3%E4%BB%B7%E5%87%BD%E6%95%B0
3、正则化https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3-%E6%AD%A3%E5%88%99%E5%8C%96
4、反向传播BPhttps://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#5-bp%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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/MachineLearning_Python#8-%E9%A2%84%E6%B5%8B
9、输出结果https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#%E5%9B%9B-svm%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/expectation-maximization/MachineLearning_Python#1-%E4%BB%A3%E4%BB%B7%E5%87%BD%E6%95%B0
2、Large Marginhttps://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2-large-margin
3、SVM Kernel（核函数）https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3-svm-kernel%E6%A0%B8%E5%87%BD%E6%95%B0
4、使用中的模型代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4-%E4%BD%BF%E7%94%A8%E4%B8%AD%E7%9A%84%E6%A8%A1%E5%9E%8B%E4%BB%A3%E7%A0%81
5、运行结果https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#%E4%BA%94-k-means%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95
1、聚类过程https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/MachineLearning_Python#%E5%85%AD-pca%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/expectation-maximization/MachineLearning_Python#1-%E7%94%A8%E5%A4%84
2、2D-->1D，nD-->kDhttps://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2-2d-1dnd-kd
3、主成分分析PCA与线性回归的区别https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#4-pca%E9%99%8D%E7%BB%B4%E8%BF%87%E7%A8%8B
5、数据恢复https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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 Detectionhttps://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/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%83
2、异常检测算法https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python#3-%E8%AF%84%E4%BB%B7%E7%9A%84%E5%A5%BD%E5%9D%8F%E4%BB%A5%E5%8F%8A%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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/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/expectation-maximization/MachineLearning_Python/blob/master/LinearRegression
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#一线性回归
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/LinearRegression/LinearRegression.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1代价函数
https://camo.githubusercontent.com/4c31a90ef7d5a94b36a26f6ca594cbd95a4d9893b419cc30d3b86092d2ba3e04/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d4a2532382535437468657461253230253239253230253344253230253543667261632537423125374425374225374232253742253543746578742537426d25374425374425374425374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253742253742253238253742685f253543746865746125323025374425323825374278253545253742253238692532392537442537442532392532302d253230253742792535452537422532386925323925374425374425323925374425354532253744253744253230
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2梯度下降算法
https://camo.githubusercontent.com/b02007afc938ffd408a4a8bee48428c7bc0b32b179f48e91dc0344f99d26d333/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d25374225374225354374686574612532305f6a253744253744
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3均值归一化
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4最终运行结果
https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python/blob/master/LinearRegression/LinearRegression_scikit-learn.py
https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python/blob/master/LogisticRegression
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#二逻辑回归
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1代价函数-1
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_01.png
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_02.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2梯度
https://camo.githubusercontent.com/b4c704104dbc22b23f487f159ef20f29a99de6214668e0f428609a7901a4af9c/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253543667261632537422537422535437061727469616c2532304a25323825354374686574612532302532392537442537442537422537422535437061727469616c25323025374225354374686574612532305f6a25374425374425374425323025334425323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253542253238253742685f253543746865746125323025374425323825374278253545253742253238692532392537442537442532392532302d2532302537427925354525374225323869253239253744253744253239785f6a25354525374225323869253239253744253544253744253230
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_03.jpg
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3正则化
https://camo.githubusercontent.com/b8584d987717f64f58a44a61e437e0af63a13c6a3bc7511d7b56b9f118b0b938/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d4a25323825354374686574612532302532392532302533442532302532302d25323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d25323025374225354225374279253545253742253238692532392537442537442535436c6f67253230253238253742685f25354374686574612532302537442532382537427825354525374225323869253239253744253744253239253230253242253230253238312532302d25323025374425323025374279253545253742253238692532392537442537442532392535436c6f67253230253238312532302d253230253742685f25354374686574612532302537442532382537427825354525374225323869253239253744253744253239253544253230253242253230253543667261632537422535436c616d626461253230253744253742253742326d25374425374425354373756d2535436c696d6974735f2537426a253230253344253230312537442535456e25323025374225354374686574612532305f6a25354532253744253230
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4s型函数即
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#5映射为多项式
https://camo.githubusercontent.com/c13817ab54a19a19c1d5ab73af10fd7e059ddf150f07af5d90bd8c6f66bfb656/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d31253230253242253230253742785f31253744253230253242253230253742785f32253744253230253242253230785f3125354532253230253242253230253742785f31253744253742785f32253744253230253242253230785f3225354532
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#6使用scipy的优化方法
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#7运行结果
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_04.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_05.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_06.png
https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_scikit-learn.py
https://patch-diff.githubusercontent.com/expectation-maximization/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_OneVsAllhttps://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/LogisticRegression
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#逻辑回归_手写数字识别_onevsall
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_OneVsAll.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1随机显示100个数字
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_08.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_09.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2onevsall
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_11.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3手写数字识别
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_10.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4预测
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#5运行结果
https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python/blob/master/LogisticRegression/LogisticRegression_OneVsAll_scikit-learn.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#6使用scikit-learn库中的逻辑回归模型实现
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/LogisticRegression_13.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#三bp神经网络
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/NeuralNetwok/NeuralNetwork.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1神经网络model
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/NeuralNetwork_01.png
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2代价函数
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3正则化-1
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/NeuralNetwork_02.png
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4反向传播bp
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#5bp可以求梯度的原因
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#6梯度检查
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#7权重的随机初始化
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#8预测
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#9输出结果
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#四svm支持向量机
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1代价函数-2
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2large-margin
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4使用scikit-learn中的svm模型代码
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/SVM/SVM_scikit-learn.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#5运行结果-1
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/SVM_09.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/SVM_10.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#五k-means聚类算法
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/K-Means/K-Menas.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1聚类过程
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/K-Means_01.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/K-Means_02.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/K-Means_03.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2目标函数
https://camo.githubusercontent.com/953c5f0a06ce3f5a7bd592a4dfbf3a3759343f0299c88b7bdead6d88481bab32/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d4a253238253742632535452537422532383125323925374425374425324325323025354363646f7473253230253243253742632535452537422532386d253239253744253744253243253742755f3125374425324325323025354363646f7473253230253243253742755f6b25374425323925323025334425323025354366726163253742312537442537426d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d25323025374225374325374325374278253545253742253238692532392537442537442532302d253230253742755f253742253742632535452537422532386925323925374425374425374425374425374325374225374325354532253744253744253230
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/K-Means_07.png
https://camo.githubusercontent.com/adfd8af7566c70530283de7ce0eae9564fbfb7aea85797ea53925bd44d41692d/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d2537426325354525374225323869253239253744253744
https://camo.githubusercontent.com/bab62cd41e2de99f132796d387b7275c4d95df955e57f1435b43d6a110dd0a51/687474703a2f2f63686172742e617069732e676f6f676c652e636f6d2f63686172743f6368743d7478266368733d317830266368663d62672c732c4646464646463030266368636f3d3030303030302663686c3d253742755f69253744
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3聚类中心的选择
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4聚类个数k的选择
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/K-Means_04.png
https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python/blob/master/K-Means/K-Means_scikit-learn.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#6使用scikit-learn库中的线性模型实现聚类
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#7运行结果-1
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/K-Means_05.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/K-Means_06.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#六pca主成分分析降维
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/PCA/PCA.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1用处
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#22d--1dnd--kd
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/PCA_01.png
https://camo.githubusercontent.com/9d7fb819f5a864db20e89fb0591b20fef330019f8b4ad1f37651a1afb2ed049d/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253234253234253742752535452537422532383125323925374425374425324325374275253545253742253238322532392537442537442532302535436c646f7473253230253742752535452537422532386b253239253744253744253234253234
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3主成分分析pca与线性回归的区别
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4pca降维过程
https://camo.githubusercontent.com/24c2003379ff16ddca7f830b60e361e5b9fd0c6e90c4b19eae9fbc078845be2c/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253234253234253742253543726d253742782537442537445f6a25354525374225323869253239253744253230253344253230253742253742253742253543726d253742782537442537445f6a253545253742253238692532392537442532302d253230253742755f6a2537442537442532302535436f766572253230253742253742735f6a253744253744253744253234253234
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https://camo.githubusercontent.com/1d76edad1e9c4be443f5e98916b20170a780c1d4f92f4d912cc8fe5ca73ad613/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f2532342532342535435369676d6125323025334425323055532537425625354554253744253234253234
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/PCA_02.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/PCA_03.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#5数据恢复
https://camo.githubusercontent.com/133403f742334ae1c860164abe971cd06e0f5c1a341e0ecd31de6057f795520b/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253543666e5f636d2532302532342532342537425a25354525374225323869253239253744253744253230253344253230555f253742726564756365253744253545542a2537425825354525374225323869253239253744253744253234253234
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#6主成分个数的选择即要降的维度
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https://camo.githubusercontent.com/09ce06f4f0d56ab9690e9a4b677b9c1bdef2dc77f5f83d10e72ca097a21eb61c/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253543666e5f636d253230253234253234253742312532302535436f7665722532306d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d253230253742253743253743253742782535452537422532386925323925374425374425374325374225374325354532253744253744253230253234253234
https://camo.githubusercontent.com/b3881cee512c32154d7a6559dbddb94c674c29cb6735a9ff87d7d87f75b54081/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f6769662e6c617465783f253543666e5f636d253230253234253234253742253742253742312532302535436f7665722532306d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d25323025374225374325374325374278253545253742253238692532392537442537442532302d253230785f253742617070726f7825374425354525374225323869253239253744253743253742253743253545322537442537442532302537442532302535436f766572253230253742253742312532302535436f7665722532306d25374425354373756d2535436c696d6974735f25374269253230253344253230312537442535456d2532302537422537432537432537427825354525374225323869253239253744253744253743253742253743253545322537442537442532302537442537442532302535436c65253230302e3031253234253234
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#7使用建议
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#8运行结果
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/PCA_04.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/PCA_05.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/PCA_06.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/PCA_07.png
https://patch-diff.githubusercontent.com/expectation-maximization/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/expectation-maximization/MachineLearning_Python/blob/master/PCA/PCA.py_scikit-learn.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#9使用scikit-learn库中的pca实现降维
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#七异常检测-anomaly-detection
全部代码https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/AnomalyDetection/AnomalyDetection.py
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#1高斯分布正态分布gaussian-distribution
https://camo.githubusercontent.com/271e086af38ca74716a26e382ab30b1587640c68d13c10aeec4dd2c3e3993b7d/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532347025323878253239253230253344253230253742312532302535436f766572253230253742253543737172742532302537423225354370692532302537442532302535437369676d61253230253744253744253742652535452537422532302d253230253742253742253742253742253238782532302d25323075253239253744253545322537442537442532302535436f766572253230253742322537422535437369676d6125323025354532253744253744253744253744253744253234253234
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#2异常检测算法
https://camo.githubusercontent.com/49c23c2fe3ead190757ced32050f7ca219c3f027a730bff8defe7ad3fee5f59e/687474703a2f2f6c617465782e636f6465636f67732e636f6d2f706e672e6c617465783f253543666e5f636d2532302532342532342535432537422532302537427825354525374225323831253239253744253744253243253742782535452537422532383225323925374425374425324325323025354363646f7473253230253742782535452537422532386d253239253744253744253543253744253230253234253234
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#3评价px的好坏以及ε的选取
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_01.png
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#4选择使用什么样的feature单元高斯分布
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#5多元高斯分布
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_04.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_02.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_03.png
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_05.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_07.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_06.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#6单元和多元高斯分布特点
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https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python#7程序运行结果
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_08.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_09.png
https://patch-diff.githubusercontent.com/expectation-maximization/MachineLearning_Python/blob/master/images/AnomalyDetection_10.png
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