├── EM算法 └── EM算法.py ├── Mnist train and test.rar ├── README.md ├── k近邻KNN ├── KNN - 带权值的近邻点优化.py └── KNN - 线性扫描.py ├── my_book └── logistic_regression.md ├── sklearn ├── sklearn - 特征工程.ipynb └── sklearn - 算法.ipynb ├── tensorflow ├── tensorflow2.0 - CNN.ipynb ├── tensorflow2.0 - Keras高层接口.ipynb ├── tensorflow2.0 - RNN.ipynb ├── tensorflow2.0 - tensorflow2.0的特点.ipynb ├── tensorflow2.0 - 反向传播算法.ipynb ├── tensorflow2.0 - 可视化.ipynb ├── tensorflow2.0 - 基础1 - 创建和数据类型及其应用.ipynb ├── tensorflow2.0 - 基础2 - 索引切片_维度转换_数学运算.ipynb ├── tensorflow2.0 - 基础3 - 合并_分割_数据统计_填充_限幅.ipynb ├── tensorflow2.0 - 基础4 - 高级函数.ipynb ├── tensorflow2.0 - 模型乐园_修改现存模型训练新数据.ipynb ├── tensorflow2.0 - 测量工具.ipynb ├── tensorflow2.0 - 神经网络初级.ipynb ├── tensorflow2.0 - 神经网络构建的一般步骤.ipynb ├── tensorflow2.0 - 自定义类.ipynb └── tensorflow2.0 - 过拟合的影响.ipynb ├── 决策树Decision Tree ├── 决策树 - C4.5.py ├── 决策树 - CART.py └── 决策树 - ID3.py ├── 感知机perceptron ├── 感知机模型 - adagrad.py ├── 感知机模型 - 对偶形式.py ├── 感知机模型 - 梯度下降法.py └── 感知机模型 - 随机梯度下降法.py ├── 提升树Boosting Tree └── Adaboost.py ├── 支持向量机SVM └── 支持向量机 - SMO - 高斯核函数.py ├── 朴素贝叶斯Bayes └── 朴素贝叶斯 - 贝叶斯估计.py └── 逻辑斯蒂回归Logistic Regression ├── One_VS_All.py ├── One_VS_One.py ├── 按样本划分的并行化.py ├── 按特征划分的并行化.py ├── 逻辑斯蒂回归 - Adam.py ├── 逻辑斯蒂回归 - RMSProp.py ├── 逻辑斯蒂回归 - SGDM.py └── 逻辑斯蒂回归 - 随机梯度下降法.py /EM算法/EM算法.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Zhouxiaonnan/machine-learning-notesandcode/HEAD/EM算法/EM算法.py -------------------------------------------------------------------------------- /Mnist train and test.rar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Zhouxiaonnan/machine-learning-notesandcode/HEAD/Mnist train and test.rar -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Zhouxiaonnan/machine-learning-notesandcode/HEAD/README.md -------------------------------------------------------------------------------- /k近邻KNN/KNN - 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