▍1. sap-backend-master
说明： 这个程序用来实现排课，利用的是·遗传算法，但这只是一个简单的程序(to deal with timetable scheduing)
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▍4. Clustering
1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。 2) 使用C-Means算法对所有数据点进行聚类。C=3。 任务2（必做）： 使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。 任务3（全做）： 1) 参考数据文件第三列的类标签，使用聚类有效性评价的外部方法Normalized Mutual Information指标，分别计算任务1和任务2聚类结果的有效性。 2) 使用聚类有效性评价的内部方法Xie-Beni指标，分别计算任务1和任务2聚类结果的有效性。(The main results are as follows: 1) the condensed hierarchical clustering algorithm (that is, the minimum spanning tree algorithm) is used to cluster all the data points, and finally it is grouped into three categories. Any of the single linkage,complete linkage,average linkage or average group linkage methods can be selected for the definition of dissimilarity. 2) using C-Means algorithm to cluster all data points. C = 3.)
▍5. Clustering
说明： 1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。 2) 使用C-Means算法对所有数据点进行聚类。C=3。 任务2（必做）： 使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。 任务3（全做）： 1) 参考数据文件第三列的类标签，使用聚类有效性评价的外部方法Normalized Mutual Information指标，分别计算任务1和任务2聚类结果的有效性。 2) 使用聚类有效性评价的内部方法Xie-Beni指标，分别计算任务1和任务2聚类结果的有效性。(The main results are as follows: 1) the condensed hierarchical clustering algorithm (that is, the minimum spanning tree algorithm) is used to cluster all the data points, and finally it is grouped into three categories. Any of the single linkage,complete linkage,average linkage or average group linkage methods can be selected for the definition of dissimilarity. 2) using C-Means algorithm to cluster all data points. C = 3.)
▍8. kalman
基于卡尔曼滤波的变形监测数据处理的python程序(Data Processing of Deformation Monitoring Based on Kalman Filter)
▍9. kalman
说明： 基于卡尔曼滤波的变形监测数据处理的python程序(Data Processing of Deformation Monitoring Based on Kalman Filter)
▍12. Python与机器学习实战
python与机器学习实战教程，机器学习通过Python语言实现，通过大量的实例再现机器学习强大的生命力(Python and Machine Learning Practical Course. Machine Learning is realized by Python Language, and the powerful vitality of machine learning is reappeared through a large number of examples.)
▍13. Python与机器学习实战
说明： python与机器学习实战教程，机器学习通过Python语言实现，通过大量的实例再现机器学习强大的生命力(Python and Machine Learning Practical Course. Machine Learning is realized by Python Language, and the powerful vitality of machine learning is reappeared through a large number of examples.)
▍14. kaggle_diabetic-master
A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.
▍15. kaggle_diabetic-master
说明： A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.
▍16. mnist
用深度学习的方法识别数字，神经网络使用CNN，数据集mnist(Identification number)
▍20. Python机器学习算法—赵志勇
Python机器学习算法—赵志勇，包含电子版书籍和随书代码，供大家学习。(Python Machine Learning Algorithms - Zhao Zhiyong, including electronic books and accompanying codes for everyone to learn.)
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