{"id":36530910,"url":"https://github.com/wepe/MachineLearning","last_synced_at":"2026-01-18T21:00:44.269Z","repository":{"id":24204353,"uuid":"27595858","full_name":"wepe/MachineLearning","owner":"wepe","description":"Basic Machine Learning and Deep 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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：\n\n   [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)\n      \n   [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)\n\n   [FaceRecognition_CNN(olivettifaces)](https://github.com/wepe/MachineLearning-Demo/tree/master/DeepLearning%20Tutorials/FaceRecognition_CNN(olivettifaces))\n      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)\n\n\n   [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)\n\n   [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)\n\n   [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)\n\n- **PCA**\n\n   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)\n\n- **kNN**\n      \n   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)\n\n- **logistic regression**\n\n   - 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)\n\n   - 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)\n\n- **ManifoldLearning**\n\n\t[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)\n     \n- **SVM**    \n\n\t[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)\n\n    [SVM by SMO](./SVM/SVM_by_SMO) - 用SMO实现了SVM\n\n    [SVM by QP](./SVM/SVM_by_QP) - 用二次编程（QP）实现了SVM\n\n\n- **GMM**\n\n\tGMM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)\n\n- **DecisionTree**\n\n\tPython、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)\n\n- **KMeans**\n\n\t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)\n\n- **NaiveBayes**\n\n\t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)\n\n- **Ridge and Kernel Ridge**\n\n    介绍了Ridge回归和它的Kernel版本。[代码](./Ridge/kernel_ridge/kernel_ridge.py)\n\n## Contributor\n\n- [wepon](https://github.com/wepe)\n- [Gogary](https://github.com/enjoyhot)\n- [Locky](https://github.com/junlulocky)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwepe%2FMachineLearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwepe%2FMachineLearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwepe%2FMachineLearning/lists"}