Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
.ipynb_checkpoints		.ipynb_checkpoints
__pycache__		__pycache__
mldata		mldata
.gitignore		.gitignore
README.md		README.md
__init__.py		__init__.py
c10_在线性回归模型中使用梯度下降法.ipynb		c10_在线性回归模型中使用梯度下降法.ipynb
c11_向量化梯度下降法预测波士顿房价.ipynb		c11_向量化梯度下降法预测波士顿房价.ipynb
c12_随机梯度下降法.ipynb		c12_随机梯度下降法.ipynb
c13_如何调试梯度.ipynb		c13_如何调试梯度.ipynb
c14_主成分分析PCA.ipynb		c14_主成分分析PCA.ipynb
c15_求数据的前n个主成分.ipynb		c15_求数据的前n个主成分.ipynb
c16_PCA.py		c16_PCA.py
c16_高维数据映射为低维数据.ipynb		c16_高维数据映射为低维数据.ipynb
c17_使用sklearn对真实数据集PCA.ipynb		c17_使用sklearn对真实数据集PCA.ipynb
c18_mnist_knn.py		c18_mnist_knn.py
c18_更大数据集MNIST降为处理.ipynb		c18_更大数据集MNIST降为处理.ipynb
c19_pca_eigenface_get_data.py		c19_pca_eigenface_get_data.py
c19_人脸识别与特征脸.ipynb		c19_人脸识别与特征脸.ipynb
c1_KNN.py		c1_KNN.py
c1_KNNClassifier.py		c1_KNNClassifier.py
c1_K近邻算法.ipynb		c1_K近邻算法.ipynb
c20_多项式回归.ipynb		c20_多项式回归.ipynb
c21_过拟合和欠拟合.ipynb		c21_过拟合和欠拟合.ipynb
c22_学习曲线.ipynb		c22_学习曲线.ipynb
c23_验证数据集与交叉验证.ipynb		c23_验证数据集与交叉验证.ipynb
c24_模型泛化与岭回归_正则化.ipynb		c24_模型泛化与岭回归_正则化.ipynb
c25_Lasso回归.ipynb		c25_Lasso回归.ipynb
c26_LogisticRegression.py		c26_LogisticRegression.py
c26_逻辑回归和决策边界.ipynb		c26_逻辑回归和决策边界.ipynb
c27_在逻辑回归中使用多项式特征.ipynb		c27_在逻辑回归中使用多项式特征.ipynb
c28_Scikit-learn中的逻辑回归.ipynb		c28_Scikit-learn中的逻辑回归.ipynb
c29_OvR和OvO.ipynb		c29_OvR和OvO.ipynb
c2_model_selection.py		c2_model_selection.py
c2_train_test_split.ipynb		c2_train_test_split.ipynb
c30_分类问题的评判标准.ipynb		c30_分类问题的评判标准.ipynb
c31_PR curve.ipynb		c31_PR curve.ipynb
c32_ROC curve.ipynb		c32_ROC curve.ipynb
c32_metrics.py		c32_metrics.py
c33_多分类问题中的混淆矩阵.ipynb		c33_多分类问题中的混淆矩阵.ipynb
c34_SVM支持向量机.ipynb		c34_SVM支持向量机.ipynb
c35_SVM使用多项式特征_核函数概念.ipynb		c35_SVM使用多项式特征_核函数概念.ipynb
c36_sklearn中的高斯核函数.ipynb		c36_sklearn中的高斯核函数.ipynb
c37_SVM解决回归问题.ipynb		c37_SVM解决回归问题.ipynb
c38_决策树和信息熵.ipynb		c38_决策树和信息熵.ipynb
c39_使用信息熵寻找最优划分.ipynb		c39_使用信息熵寻找最优划分.ipynb
c3_accuracy_score.py		c3_accuracy_score.py
c3_classify_accuracy.ipynb		c3_classify_accuracy.ipynb
c40_使用基尼系数进行划分.ipynb		c40_使用基尼系数进行划分.ipynb
c41_CART与决策树中的超参数.ipynb		c41_CART与决策树中的超参数.ipynb
c42_决策树解决回归问题.ipynb		c42_决策树解决回归问题.ipynb
c43_决策树的局限性.ipynb		c43_决策树的局限性.ipynb
c44_集成学习.ipynb		c44_集成学习.ipynb
c45_Bagging_Pasting_OOB_随机森林.ipynb		c45_Bagging_Pasting_OOB_随机森林.ipynb
c46_Boosting_Stacking.ipynb		c46_Boosting_Stacking.ipynb
c4_knn_test_grid_search.py		c4_knn_test_grid_search.py
c4_超参数_Grid_Search.ipynb		c4_超参数_Grid_Search.ipynb
c5_StandardScaler.py		c5_StandardScaler.py
c5_数据归一化.ipynb		c5_数据归一化.ipynb
c6_SimpleLinearRegression.py		c6_SimpleLinearRegression.py
c6_实现简单线性回归.ipynb		c6_实现简单线性回归.ipynb
c7_衡量回归算法的标准.ipynb		c7_衡量回归算法的标准.ipynb
c8_1多元线性回归.ipynb		c8_1多元线性回归.ipynb
c8_2线性回归的可解性和更多思考.ipynb		c8_2线性回归的可解性和更多思考.ipynb
c8_LinearRegression.py		c8_LinearRegression.py
c9_梯度下降法.ipynb		c9_梯度下降法.ipynb

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AI_Machine_Learning_Muke

Python Version : 3.6.3

jupyterlab 0.32.1
jupyterlab-launcher 0.10.5
numpy 1.14.0
scikit-learn 0.19.1
matplotlib 2.1.2

mldata 文件夹下的数据集大概52MB

lfw_home 200+MB, 可以用迅雷点击下载

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