文件列表:

LICENSE (1078, 2019-03-28)
classic-machine-learning (0, 2019-03-28)
classic-machine-learning\birch_cluster.ipynb (120401, 2019-03-28)
classic-machine-learning\bpr.ipynb (703271, 2019-03-28)
classic-machine-learning\dbscan_cluster.ipynb (165300, 2019-03-28)
classic-machine-learning\decision_tree_classifier.ipynb (172252, 2019-03-28)
classic-machine-learning\decision_tree_classifier_1.ipynb (171946, 2019-03-28)
classic-machine-learning\fp_tree_prefixspan.ipynb (9883, 2019-03-28)
classic-machine-learning\kmeans_cluster.ipynb (493007, 2019-03-28)
classic-machine-learning\knn_classifier.ipynb (146517, 2019-03-28)
classic-machine-learning\lda.ipynb (227607, 2019-03-28)
classic-machine-learning\linear-regression.ipynb (53659, 2019-03-28)
classic-machine-learning\lle.ipynb (494023, 2019-03-28)
classic-machine-learning\matrix_factorization.ipynb (481267, 2019-03-28)
classic-machine-learning\native_bayes.ipynb (2031, 2019-03-28)
classic-machine-learning\pca.ipynb (78197, 2019-03-28)
classic-machine-learning\regression_production_example.ipynb (73843, 2019-03-28)
classic-machine-learning\ridge_regression.ipynb (43609, 2019-03-28)
classic-machine-learning\ridge_regression_1.ipynb (4621, 2019-03-28)
classic-machine-learning\spectral_cluster.ipynb (4633, 2019-03-28)
classic-machine-learning\svm_classifier.ipynb (243879, 2019-03-28)
data (0, 2019-03-28)
data\CCPP.zip (3674852, 2019-03-28)
data\nlp_test0.txt (392, 2019-03-28)
data\nlp_test2.txt (264, 2019-03-28)
data\train_modified.zip (255259, 2019-03-28)
ensemble-learning (0, 2019-03-28)
ensemble-learning\adaboost-classifier.ipynb (114586, 2019-03-28)
ensemble-learning\gbdt_classifier.ipynb (18468, 2019-03-28)
ensemble-learning\random_forest_classifier.ipynb (15904, 2019-03-28)
mathematics (0, 2019-03-28)
mathematics\mcmc_2.ipynb (111837, 2019-03-28)
mathematics\mcmc_3_4.ipynb (88580, 2019-03-28)
mathematics\random_data_generation.ipynb (162095, 2019-03-28)
model-in-product (0, 2019-03-28)
model-in-product\sklearn-jpmml (0, 2019-03-28)
model-in-product\sklearn-jpmml\PMML_Example.ipynb (2686, 2019-03-28)
model-in-product\sklearn-jpmml\demo.pmml (3055, 2019-03-28)
... ...

# 刘建平Pinard的博客配套代码 http://www.cnblogs.com/pinard 刘建平Pinard 之前不少朋友反应我博客中的代码都是连续的片段,不好学习,因此这里把文章和代码做一个整理。 代码有部分来源于网络,已加上相关方版权信息。部分为自己原创,已加上我的版权信息。 ## 目录 * [机器学习基础与回归算法](#2) * [机器学习分类算法](#3) * [机器学习聚类算法](#4) * [机器学习降维算法](#5) * [机器学习集成学习算法](#6) * [数学统计学](#7) * [机器学习关联算法](#8) * [机器学习推荐算法](#9) * [深度学习算法](#10) * [自然语言处理算法](#11) * [强化学习算法](#1) * [特征工程与算法落地](#12) ## 注意 2016-2017年写的博客使用的python版本是2.7, 2018年因为TensorFlow对Python3的一些要求,所以写博客使用的Python版本是3.6。少部分2016,2017年的博客代码无法找到,重新用Python3.6跑过上传,因此可能会出现和博客中代码稍有不一致的地方,主要涉及到print的语法和range的用法,若遇到问题,稍微修改即可跑通。

强化学习文章与代码::

|文章 | 代码| ---|--- [强化学习(一)模型基础](https://www.cnblogs.com/pinard/p/9385570.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/introduction.py) [强化学习(二)马尔科夫决策过程(MDP)](https://www.cnblogs.com/pinard/p/9426283.html) | 无 [强化学习(三)用动态规划(DP)求解](https://www.cnblogs.com/pinard/p/9463815.html) | 无 [强化学习(四)用蒙特卡罗法(MC)求解](https://www.cnblogs.com/pinard/p/9492***0.html) | 无 [强化学习(五)用时序差分法(TD)求解](https://www.cnblogs.com/pinard/p/952***28.html) | 无 [强化学习(六)时序差分在线控制算法SARSA](https://www.cnblogs.com/pinard/p/9614290.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/sarsa_windy_world.py) [强化学习(七)时序差分离线控制算法Q-Learning](https://www.cnblogs.com/pinard/p/9669263.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/q_learning_windy_world.py) [强化学习(八)价值函数的近似表示与Deep Q-Learning](https://www.cnblogs.com/pinard/p/9714655.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/dqn.py) [强化学习(九)Deep Q-Learning进阶之Nature DQN](https://www.cnblogs.com/pinard/p/9756075.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/nature_dqn.py) [强化学习(十)Double DQN (DDQN)](https://www.cnblogs.com/pinard/p/9778063.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/ddqn.py) [强化学习(十一) Prioritized Replay DQN](https://www.cnblogs.com/pinard/p/9797695.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/ddqn_prioritised_replay.py) [强化学习(十二) Dueling DQN](https://www.cnblogs.com/pinard/p/9923859.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/duel_dqn.py) [强化学习(十三) 策略梯度(Policy Gradient)](https://www.cnblogs.com/pinard/p/10137696.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/policy_gradient.py) [强化学习(十四) Actor-Critic](https://www.cnblogs.com/pinard/p/10272023.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/actor_critic.py) [强化学习(十五) A3C](https://www.cnblogs.com/pinard/p/10334127.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/a3c.py) [强化学习(十六) 深度确定性策略梯度(DDPG)](https://www.cnblogs.com/pinard/p/10345762.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/reinforcement-learning/ddpg.py) [强化学习(十七) 基于模型的强化学习与Dyna算法框架](https://www.cnblogs.com/pinard/p/10384424.html) | 无 [强化学习(十八) 基于模拟的搜索与蒙特卡罗树搜索(MCTS)](https://www.cnblogs.com/pinard/p/10470571.html) | 无 [强化学习(十九) AlphaGo Zero强化学习原理](https://www.cnblogs.com/pinard/p/10609228.html) | 无

机器学习基础与回归算法文章与代码:

|文章 | 代码| ---|--- [梯度下降(Gradient Descent)小结](https://www.cnblogs.com/pinard/p/5970503.html) | 无 [最小二乘法小结](https://www.cnblogs.com/pinard/p/5976811.html) |无 [交叉验证(Cross Validation)原理小结](https://www.cnblogs.com/pinard/p/5992719.html) | 无 [精确率与召回率,RoC曲线与PR曲线](https://www.cnblogs.com/pinard/p/5993450.html) |无 [线性回归原理小结](https://www.cnblogs.com/pinard/p/6004041.html) |无 [机器学习研究与开发平台的选择](https://www.cnblogs.com/pinard/p/6007200.html) | 无 [scikit-learn 和pandas 基于windows单机机器学习环境的搭建](https://www.cnblogs.com/pinard/p/6013484.html) |无 [用scikit-learn和pandas学习线性回归](https://www.cnblogs.com/pinard/p/6016029.html) |[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/linear-regression.ipynb) [Lasso回归算法: 坐标轴下降法与最小角回归法小结](https://www.cnblogs.com/pinard/p/6018889.html) | 无 [用scikit-learn和pandas学习Ridge回归](https://www.cnblogs.com/pinard/p/6023000.html) | [代码1](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/ridge_regression_1.ipynb) [代码2](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/ridge_regression.ipynb) [scikit-learn 线性回归算法库小结](https://www.cnblogs.com/pinard/p/6026343.html)|无 [异常点检测算法小结](https://www.cnblogs.com/pinard/p/93141***.html)|无

机器学习分类算法文章与代码:

|文章 | 代码| ---|--- [逻辑回归原理小结](https://www.cnblogs.com/pinard/p/6029432.html) |无 [scikit-learn 逻辑回归类库使用小结](https://www.cnblogs.com/pinard/p/6035872.html) |无 [感知机原理小结](https://www.cnblogs.com/pinard/p/6042320.html) |无 [决策树算法原理(上)](https://www.cnblogs.com/pinard/p/6050306.html) |无 [决策树算法原理(下)](https://www.cnblogs.com/pinard/p/6053344.html)|无 [scikit-learn决策树算法类库使用小结](https://www.cnblogs.com/pinard/p/6056319.html) |[代码1](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/decision_tree_classifier.ipynb) [代码2](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/decision_tree_classifier_1.ipynb) [K近邻法(KNN)原理小结](https://www.cnblogs.com/pinard/p/6061661.html) |无 [scikit-learn K近邻法类库使用小结](https://www.cnblogs.com/pinard/p/6065607.html) |[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/knn_classifier.ipynb) [朴素贝叶斯算法原理小结](https://www.cnblogs.com/pinard/p/6069267.html) |无 [scikit-learn 朴素贝叶斯类库使用小结](https://www.cnblogs.com/pinard/p/6074222.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/native_bayes.ipynb) [最大熵模型原理小结](https://www.cnblogs.com/pinard/p/6093948.html)|无 [支持向量机原理(一) 线性支持向量机](https://www.cnblogs.com/pinard/p/6097604.html)|无 [支持向量机原理(二) 线性支持向量机的软间隔最大化模型](https://www.cnblogs.com/pinard/p/6100722.html)|无 [支持向量机原理(三)线性不可分支持向量机与核函数](https://www.cnblogs.com/pinard/p/6103615.html)|无 [支持向量机原理(四)SMO算法原理](https://www.cnblogs.com/pinard/p/6111471.html)|无 [支持向量机原理(五)线性支持回归](https://www.cnblogs.com/pinard/p/6113120.html)|无 [scikit-learn 支持向量机算法库使用小结](https://www.cnblogs.com/pinard/p/6117515.html)|无 [支持向量机高斯核调参小结](https://www.cnblogs.com/pinard/p/6126077.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/svm_classifier.ipynb)

数学统计学文章与代码:

|文章 | 代码| ---|--- [机器学习算法的随机数据生成](https://www.cnblogs.com/pinard/p/6047802.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/random_data_generation.ipynb) [MCMC(一)蒙特卡罗方法](https://www.cnblogs.com/pinard/p/6625739.html)|无 [MCMC(二)马尔科夫链](https://www.cnblogs.com/pinard/p/6632399.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/mcmc_2.ipynb) [MCMC(三)MCMC采样和M-H采样](https://www.cnblogs.com/pinard/p/6638955.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/mcmc_3_4.ipynb) [MCMC(四)Gibbs采样](https://www.cnblogs.com/pinard/p/6***5766.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/mathematics/mcmc_3_4.ipynb)

机器学习集成学习文章与代码:

|文章 | 代码| ---|--- [集成学习原理小结](https://www.cnblogs.com/pinard/p/6131423.html) | 无 [集成学习之Adaboost算法原理小结](https://www.cnblogs.com/pinard/p/6133937.html) | 无 [scikit-learn Adaboost类库使用小结](https://www.cnblogs.com/pinard/p/6136914.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/adaboost-classifier.ipynb) [梯度提升树(GBDT)原理小结](https://www.cnblogs.com/pinard/p/6140514.html) | 无 [scikit-learn 梯度提升树(GBDT)调参小结](https://www.cnblogs.com/pinard/p/6143927.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/gbdt_classifier.ipynb) [Bagging与随机森林算法原理小结](https://www.cnblogs.com/pinard/p/6156009.html) | 无 [scikit-learn随机森林调参小结](https://www.cnblogs.com/pinard/p/6160412.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/random_forest_classifier.ipynb)

机器学习聚类算法文章与代码:

|文章 | 代码| ---|--- [K-Means聚类算法原理](https://www.cnblogs.com/pinard/p/61***214.html)|无 [用scikit-learn学习K-Means聚类](https://www.cnblogs.com/pinard/p/6169370.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/kmeans_cluster.ipynb) [BIRCH聚类算法原理](https://www.cnblogs.com/pinard/p/6179132.html)|无 [用scikit-learn学习BIRCH聚类](https://www.cnblogs.com/pinard/p/6200579.html) | [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/birch_cluster.ipynb) [DBSCAN密度聚类算法](https://www.cnblogs.com/pinard/p/6208966.html)|无 [用scikit-learn学习DBSCAN聚类](https://www.cnblogs.com/pinard/p/6217852.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/dbscan_cluster.ipynb) [谱聚类(spectral clustering)原理总结](https://www.cnblogs.com/pinard/p/62215***.html) |无 [用scikit-learn学习谱聚类](https://www.cnblogs.com/pinard/p/6235920.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/spectral_cluster.ipynb)

机器学习降维算法文章与代码:

|文章 | 代码| ---|--- [主成分分析(PCA)原理总结](https://www.cnblogs.com/pinard/p/6239403.html)|无 [用scikit-learn学习主成分分析(PCA)](https://www.cnblogs.com/pinard/p/6243025.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/pca.ipynb) [线性判别分析LDA原理总结](https://www.cnblogs.com/pinard/p/6244265.html)|无 [用scikit-learn进行LDA降维](https://www.cnblogs.com/pinard/p/6249328.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/lda.ipynb) [奇异值分解(SVD)原理与在降维中的应用](https://www.cnblogs.com/pinard/p/6251584.html)|无 [局部线性嵌入(LLE)原理总结](https://www.cnblogs.com/pinard/p/626***08.html)|无 [用scikit-learn研究局部线性嵌入(LLE)](https://www.cnblogs.com/pinard/p/6273377.html) |[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/lle.ipynb)

机器学习关联算法文章与代码:

|文章 | 代码| ---|--- [典型关联分析(CCA)原理总结](https://www.cnblogs.com/pinard/p/6288716.html)|无 [Apriori算法原理总结](https://www.cnblogs.com/pinard/p/62932***.html)|无 [FP Tree算法原理总结](https://www.cnblogs.com/pinard/p/63070***.html)|无 [PrefixSpan算法原理总结](https://www.cnblogs.com/pinard/p/6323182.html)|无 [用Spark学习FP Tree算法和PrefixSpan算法](https://www.cnblogs.com/pinard/p/6340162.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/fp_tree_prefixspan.ipynb) [日志和告警数据挖掘经验谈](https://www.cnblogs.com/pinard/p/6039099.html) | 无

机器学习推荐算法文章与代码:

|文章 | 代码| ---|--- [协同过滤推荐算法总结](https://www.cnblogs.com/pinard/p/6349233.html)|无 [矩阵分解在协同过滤推荐算法中的应用](https://www.cnblogs.com/pinard/p/6351319.html)|无 [SimRank协同过滤推荐算法](https://www.cnblogs.com/pinard/p/6362***7.html)|无 [用Spark学习矩阵分解推荐算法](https://www.cnblogs.com/pinard/p/63***932.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/matrix_factorization.ipynb) [分解机(Factorization Machines)推荐算法原理](https://www.cnblogs.com/pinard/p/6370127.html)|无 [贝叶斯个性化排序(BPR)算法小结](https://www.cnblogs.com/pinard/p/9128682.html)|无 [用tensorflow学习贝叶斯个性化排序(BPR)](https://www.cnblogs.com/pinard/p/9163481.html)| [代码](https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/bpr.ipynb)

深度学习算法文章与代码:

|文章 | 代码| ---|--- [深度神经网络(DNN)模型与前向传播算法](https://www.cnblogs.com/pinard/p/***18668.html)|无 [深度神经网络(DNN)反向传播算法(BP)](https://www.cnblogs.com/pinard/p/***22831.html)|无 [深度神经网络(DNN)损失函数和激活函数的选择](https://www.cnblogs.com/pinard/p/***37495.html)|无 [深度神经网络(DNN)的正则化](https://www.cnblogs.com/pinard/p/***72666.html)|无 [卷积神经网络(CNN)模型结构](https://www.cnblogs.com/pinard/p/***83207.html)|无 [卷积神经网络(CNN)前向传播算法](https://www.cnblogs.com/pinard/p/***89633.html)|无 [卷积神经网络(CNN)反向传播算法](https://www.cnblogs.com/pinard/p/***94810.html)|无 [循环神经网络(RNN)模型与前向反向传播算法](https://www.cnblogs.com/pinard/p/6509630.html)|无 [LSTM模型与前向反向传播算法](https://www.cnblogs.com/pinard/p/6519110.html)|无 [受限玻尔兹曼机(RBM)原理总结](https://www.cnblogs.com/pinard/p/6530523.html)|无

自然语言处理文章与代码:

|文章 | 代码| ---|--- [文本挖掘的分词原理](https://www.cnblogs.com/pinard/p/6677078.html)|无 [文本挖掘预处理之向量化与Hash Trick](https://www.cnblogs.com/pinard/p/6688348.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/hash_trick.ipynb) [文本挖掘预处理之TF-IDF](https://www.cnblogs.com/pinard/p/6693230.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/tf-idf.ipynb) [中文文本挖掘预处理流程总结](https://www.cnblogs.com/pinard/p/6744056.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/chinese_digging.ipynb) [英文文本挖掘预处理流程总结](https://www.cnblogs.com/pinard/p/6756534.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/english_digging.ipynb) [文本主题模型之潜在语义索引(LSI)](https://www.cnblogs.com/pinard/p/6805861.html)|无 [文本主题模型之非负矩阵分解(NMF)](https://www.cnblogs.com/pinard/p/6812011.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/nmf.ipynb) [文本主题模型之LDA(一) LDA基础](https://www.cnblogs.com/pinard/p/6831308.html)|无 [文本主题模型之LDA(二) LDA求解之Gibbs采样算法](https://www.cnblogs.com/pinard/p/6867828.html)|无 [文本主题模型之LDA(三) LDA求解之变分推断EM算法](https://www.cnblogs.com/pinard/p/6873703.html)|无 [用scikit-learn学习LDA主题模型](https://www.cnblogs.com/pinard/p/6908150.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/lda.ipynb) [EM算法原理总结](https://www.cnblogs.com/pinard/p/6912636.html)|无 [隐马尔科夫模型HMM(一)HMM模型](https://www.cnblogs.com/pinard/p/6945257.html)|无 [隐马尔科夫模型HMM(二)前向后向算法评估观察序列概率](https://www.cnblogs.com/pinard/p/6955871.html)|无 [隐马尔科夫模型HMM(三)鲍姆-韦尔奇算法求解HMM参数](https://www.cnblogs.com/pinard/p/6972299.html)|无 [隐马尔科夫模型HMM(四)维特比算法解码隐藏状态序列](https://www.cnblogs.com/pinard/p/6991852.html)|无 [用hmmlearn学习隐马尔科夫模型HMM](https://www.cnblogs.com/pinard/p/7001397.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/hmm.ipynb) [条件随机场CRF(一)从随机场到线性链条件随机场](https://www.cnblogs.com/pinard/p/7048333.html)|无 [条件随机场CRF(二) 前向后向算法评估标记序列概率](https://www.cnblogs.com/pinard/p/7055072.html)|无 [条件随机场CRF(三) 模型学习与维特比算法解码](https://www.cnblogs.com/pinard/p/7068574.html)|无 [word2vec原理(一) CBOW与Skip-Gram模型基础](https://www.cnblogs.com/pinard/p/7160330.html)|无 [word2vec原理(二) 基于Hierarchical Softmax的模型](https://www.cnblogs.com/pinard/p/7243513.html)|无 [word2vec原理(三) 基于Negative Sampling的模型](https://www.cnblogs.com/pinard/p/7249903.html)|无 [用gensim学习word2vec](https://www.cnblogs.com/pinard/p/7278324.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/word2vec.ipynb)

特征工程与算法落地文章与代码:

|文章 | 代码| ---|--- [特征工程之特征选择](https://www.cnblogs.com/pinard/p/9032759.html)|无 [特征工程之特征表达](https://www.cnblogs.com/pinard/p/9061549.html)|无 [特征工程之特征预处理](https://www.cnblogs.com/pinard/p/9093890.html)|无 [用PMML实现机器学习模型的跨平台上线](https://www.cnblogs.com/pinard/p/9220199.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/model-in-product/sklearn-jpmml) [tensorflow机器学习模型的跨平台上线](https://www.cnblogs.com/pinard/p/9251296.html)|[代码](https://github.com/ljpzzz/machinelearning/blob/master/model-in-product/tensorflow-java) License MIT.

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