文件列表:

DecisionTree (0, 2017-07-28)
DecisionTree\id3_c45.py (9244, 2017-07-28)
DecisionTree\treePlotter.py (3283, 2017-07-28)
DeepLearning Tutorials (0, 2017-07-28)
DeepLearning Tutorials\FaceRecognition_CNN(olivettifaces) (0, 2017-07-28)
DeepLearning Tutorials\FaceRecognition_CNN(olivettifaces)\olivettifaces.gif (1182905, 2017-07-28)
DeepLearning Tutorials\FaceRecognition_CNN(olivettifaces)\train_CNN_olivettifaces.py (15554, 2017-07-28)
DeepLearning Tutorials\FaceRecognition_CNN(olivettifaces)\use_CNN_olivettifaces.py (7042, 2017-07-28)
DeepLearning Tutorials\Softmax_sgd(or logistic_sgd) (0, 2017-07-28)
DeepLearning Tutorials\Softmax_sgd(or logistic_sgd)\logistic_sgd.py (9456, 2017-07-28)
DeepLearning Tutorials\Softmax_sgd(or logistic_sgd)\logistic_sgd_commentate.py (19117, 2017-07-28)
DeepLearning Tutorials\cnn_LeNet (0, 2017-07-28)
DeepLearning Tutorials\cnn_LeNet\convolutional_mlp.py (12645, 2017-07-28)
DeepLearning Tutorials\cnn_LeNet\convolutional_mlp_commentate.py (20706, 2017-07-28)
DeepLearning Tutorials\dive_into_keras (0, 2017-07-28)
DeepLearning Tutorials\dive_into_keras\cnn-svm.py (2197, 2017-07-28)
DeepLearning Tutorials\dive_into_keras\cnn.py (2550, 2017-07-28)
DeepLearning Tutorials\dive_into_keras\data.py (756, 2017-07-28)
DeepLearning Tutorials\dive_into_keras\get_feature_map.py (1432, 2017-07-28)
DeepLearning Tutorials\keras_usage (0, 2017-07-28)
DeepLearning Tutorials\keras_usage\cnn.py (5233, 2017-07-28)
DeepLearning Tutorials\keras_usage\data.py (764, 2017-07-28)
DeepLearning Tutorials\mlp (0, 2017-07-28)
DeepLearning Tutorials\mlp\mlp.py (14181, 2017-07-28)
DeepLearning Tutorials\mlp\mlp_with_commentate.py (17794, 2017-07-28)
KMeans (0, 2017-07-28)
KMeans\data.pkl (60158, 2017-07-28)
KMeans\kmeans.py (7147, 2017-07-28)
KMeans\test.py (1299, 2017-07-28)
ManifoldLearning (0, 2017-07-28)
ManifoldLearning\DimensionalityReduction_DataVisualizing (0, 2017-07-28)
ManifoldLearning\DimensionalityReduction_DataVisualizing\data_visualizing.py (5507, 2017-07-28)
... ...

MachineLearning ==================== 一些常见的机器学习算法的实现代码,本人学习过程中做的总结,资历尚浅,如有错误请不吝指出。 ## 目录介绍 - **DeepLearning Tutorials** 这个文件夹下包含一些深度学习算法的实现代码,以及具体的应用实例,包含: [dive_into _keras](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials/dive_into_keras) Keras使用进阶。介绍了怎么保存训练好的CNN模型,怎么将CNN用作特征提取,怎么可视化卷积图。[文章链接](http://blog.csdn.net/u012162613/article/details/45581421), 更多进阶使用方法:[gist](https://gist.github.com/wepe/a05ad572dca002046de443061909ff7a) [keras_usage](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials/keras_usage) 介绍了一个简单易用的深度学习框架keras,用经典的Mnist分类问题对该框架的使用进行说明,训练一个CNN,总共不超过30行代码。[文章链接](http://blog.csdn.net/u012162613/article/details/45397033) [FaceRecognition_CNN(olivettifaces)](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/FaceRecognition_CNN(olivettifaces)) 将卷积神经网络CNN应用于人脸识别的一个demo,人脸数据库采用olivettifaces,CNN模型参考LeNet5,基于python+theano+numpy+PIL实现。详细介绍这个demo的文章:[文章链接](http://blog.csdn.net/u012162613/article/details/43277187) [cnn_LeNet](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/cnn_LeNet) CNN卷积神经网络算法的实现,模型为简化版的LeNet,应用于MNIST数据集(手写数字),来自于DeepLearning.net上的一个教程,基于python+theano,我用了中文将原始的代码进行详细的解读,并简单总结了CNN算法,相应的文章发在:[文章链接](http://blog.csdn.net/u012162613/article/details/43225445) [mlp](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/mlp) 多层感知机算法的实现,代码实现了最简单的三层感知机,并应用于MNIST数据集,来自DeepLearning.net上的一个教程,基于python+theano,我写了一篇文章总结介绍了MLP算法,同时用中文详细解读了原始的代码:[文章链接](http://blog.csdn.net/u012162613/article/details/43221829) [Softmax_sgd(or logistic_sgd)](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/Softmax_sgd(or%20logistic_sgd)) Softmax回归算法的实现,应用于MNIST数据集,基于Python+theano,来自DeepLearning.net上的一个教程,基于python+theano,我写了一篇文章介绍了Softmax回归算法,同时用中文详细解读了原始的代码:[文章链接](http://blog.csdn.net/u012162613/article/details/43157801) - **PCA** 基于python+numpy实现了主成份分析PCA算法,这里详细地介绍了PCA算法,以及代码开发流程:[文章链接](http://blog.csdn.net/u012162613/article/details/42177327) - **kNN** 基于python+numpy实现了K近邻算法,并将其应用在MNIST数据集上,详细的介绍:[文章链接](http://blog.csdn.net/u012162613/article/details/41768407) - **logistic regression** - 基于C++以及线性代数库Eigen实现的logistic回归,[代码](https://github.com/wepe/MachineLearning/tree/master/logistic%20regression/use_cpp_and_eigen) - 基于python+numpy实现了logistic回归(二类别),详细的介绍:[文章链接](http://blog.csdn.net/u012162613/article/details/41844495) - **ManifoldLearning** [DimensionalityReduction_DataVisualizing](https://github.com/wepe/MachineLearning/tree/master/ManifoldLearning/DimensionalityReduction_DataVisualizing) 运用多种流形学习方法将高维数据降维,并用matplotlib将数据可视化(2维和3维) - **SVM** [libsvm liblinear-usage](https://github.com/wepe/MachineLearning/tree/master/SVM/libsvm%20liblinear-usage) 对使用广泛的libsvm、liblinear的使用方法进行了总结,详细介绍:[文章链接](http://blog.csdn.net/u012162613/article/details/45206813) [SVM by SMO](./SVM/SVM_by_SMO) - 用SMO实现了SVM [SVM by QP](./SVM/SVM_by_QP) - 用二次编程(QP)实现了SVM - **GMM** GMM和k-means作为EM算法的应用,在某种程度有些相似之处,不过GMM明显学习出一些概率密度函数来,结合相关理解写成python版本,详细介绍:[文章链接](http://blog.csdn.net/gugugujiawei/article/details/45583051) - **DecisionTree** Python、Numpy、Matplotlib实现的ID3、C4.5,其中C4.5有待完善,后续加入CART。文章待总结。[代码](https://github.com/wepe/MachineLearning/tree/master/DecisionTree) - **KMeans** 介绍了聚类分析中最常用的KMeans算法(及二分KMeans算法),基于NumPy的算法实现,以及基于Matplotlib的聚类过程可视化。[文章链接](http://blog.csdn.net/u012162613/article/details/47811235) - **NaiveBayes** 朴素贝叶斯算法的理论推导,以及三种常见模型(多项式模型,高斯模型,伯努利模型)的介绍与编程实现(基于Python,Numpy)。[文章链接](http://blog.csdn.net/u012162613/article/details/48323777) - **Ridge and Kernel Ridge** 介绍了Ridge回归和它的Kernel版本。[代码](./Ridge/kernel_ridge/kernel_ridge.py) ## Contributor - [wepon](https://github.com/wepe) - [Gogary](https://github.com/enjoyhot) - [Locky](https://github.com/junlulocky)

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