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README.md

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### 第二章 感知器模型
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博客:[李航《统计学习方法》第二章——用Python实现感知器模型(MNIST数据集)](http://blog.csdn.net/wds2006sdo/article/details/51923546)
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代码:[perceptron/binary_perceptron.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/perceptron/binary_perceptron.py)
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<br>代码:[perceptron/binary_perceptron.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/perceptron/binary_perceptron.py)
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### 第三章 K近邻法
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博客:[李航《统计学习方法》第三章——用Python实现KNN算法(MNIST数据集)](http://blog.csdn.net/wds2006sdo/article/details/51933044)
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代码:[knn/knn.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/knn/knn.py)
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<br>代码:[knn/knn.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/knn/knn.py)
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### 第四章 朴素贝叶斯
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博客:[李航《统计学习方法》第四章——用Python实现朴素贝叶斯分类器(MNIST数据集)](http://blog.csdn.net/wds2006sdo/article/details/51967839)
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代码:[naive_bayes/naive_bayes.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/naive_bayes/naive_bayes.py)
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<br>代码:[naive_bayes/naive_bayes.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/naive_bayes/naive_bayes.py)
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### 第五章 决策树
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博客:[李航《统计学习方法》第五章——用Python实现决策树(MNIST数据集)](http://blog.csdn.net/wds2006sdo/article/details/52849400)
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代码:[decision_tree/decision_tree.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/decision_tree/decision_tree.py)
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<br>代码:[decision_tree/decision_tree.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/decision_tree/decision_tree.py)
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### 第六章 逻辑斯提回归
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博客:[李航《统计学习方法》第六章——用Python实现逻辑斯谛回归(MNIST数据集)](http://blog.csdn.net/wds2006sdo/article/details/53084871)
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代码:[logistic_regression/logistic_regression.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/logistic_regression/logistic_regression.py)
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<br>代码:[logistic_regression/logistic_regression.py](https://github.com/WenDesi/lihang_book_algorithm/blob/master/logistic_regression/logistic_regression.py)
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### 第六章 最大熵模型
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正在施工中。。。

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