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https://github.com/chenfork/machinelearning#刘建平pinard的博客配套代码
http://www.cnblogs.com/pinardhttp://www.cnblogs.com/pinard
https://github.com/chenfork/machinelearning#目录
机器学习基础与回归算法https://github.com/chenfork/machinelearning#2
机器学习分类算法https://github.com/chenfork/machinelearning#3
机器学习聚类算法https://github.com/chenfork/machinelearning#4
机器学习降维算法https://github.com/chenfork/machinelearning#5
机器学习集成学习算法https://github.com/chenfork/machinelearning#6
数学统计学https://github.com/chenfork/machinelearning#7
机器学习关联算法https://github.com/chenfork/machinelearning#8
机器学习推荐算法https://github.com/chenfork/machinelearning#9
深度学习算法https://github.com/chenfork/machinelearning#10
自然语言处理算法https://github.com/chenfork/machinelearning#11
强化学习算法https://github.com/chenfork/machinelearning#1
特征工程与算法落地https://github.com/chenfork/machinelearning#12
https://github.com/chenfork/machinelearning#注意
赞助我https://github.com/chenfork/machinelearning#13
https://github.com/chenfork/machinelearning#赞助我
https://github.com/chenfork/machinelearning#强化学习文章与代码
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
强化学习（三）用动态规划（DP）求解https://www.cnblogs.com/pinard/p/9463815.html
强化学习（四）用蒙特卡罗法（MC）求解https://www.cnblogs.com/pinard/p/9492980.html
强化学习（五）用时序差分法（TD）求解https://www.cnblogs.com/pinard/p/9529828.html
强化学习（六）时序差分在线控制算法SARSAhttps://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-Learninghttps://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-Learninghttps://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 DQNhttps://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 DQNhttps://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 DQNhttps://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-Critichttps://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) A3Chttps://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
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
强化学习(十九) AlphaGo Zero强化学习原理https://www.cnblogs.com/pinard/p/10609228.html
https://github.com/chenfork/machinelearning#机器学习基础与回归算法文章与代码
梯度下降（Gradient Descent）小结https://www.cnblogs.com/pinard/p/5970503.html
最小二乘法小结https://www.cnblogs.com/pinard/p/5976811.html
交叉验证(Cross Validation)原理小结https://www.cnblogs.com/pinard/p/5992719.html
精确率与召回率，RoC曲线与PR曲线https://www.cnblogs.com/pinard/p/5993450.html
线性回归原理小结https://www.cnblogs.com/pinard/p/6004041.html
机器学习研究与开发平台的选择https://www.cnblogs.com/pinard/p/6007200.html
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
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
用scikit-learn和pandas学习Ridge回归https://www.cnblogs.com/pinard/p/6023000.html
代码1https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/ridge_regression_1.ipynb
代码2https://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
异常点检测算法小结https://www.cnblogs.com/pinard/p/9314198.html
https://github.com/chenfork/machinelearning#机器学习分类算法文章与代码
逻辑回归原理小结https://www.cnblogs.com/pinard/p/6029432.html
scikit-learn 逻辑回归类库使用小结https://www.cnblogs.com/pinard/p/6035872.html
感知机原理小结https://www.cnblogs.com/pinard/p/6042320.html
决策树算法原理(上)https://www.cnblogs.com/pinard/p/6050306.html
决策树算法原理(下)https://www.cnblogs.com/pinard/p/6053344.html
scikit-learn决策树算法类库使用小结https://www.cnblogs.com/pinard/p/6056319.html
代码1https://github.com/ljpzzz/machinelearning/blob/master/classic-machine-learning/decision_tree_classifier.ipynb
代码2https://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
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
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
支持向量机原理(一) 线性支持向量机https://www.cnblogs.com/pinard/p/6097604.html
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
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
支持向量机原理(四)SMO算法原理https://www.cnblogs.com/pinard/p/6111471.html
支持向量机原理(五)线性支持回归https://www.cnblogs.com/pinard/p/6113120.html
scikit-learn 支持向量机算法库使用小结https://www.cnblogs.com/pinard/p/6117515.html
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
https://github.com/chenfork/machinelearning#数学统计学文章与代码
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
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/6645766.html
代码https://github.com/ljpzzz/machinelearning/blob/master/mathematics/mcmc_3_4.ipynb
机器学习中的矩阵向量求导(一) 求导定义与求导布局https://www.cnblogs.com/pinard/p/10750718.html
机器学习中的矩阵向量求导(二) 矩阵向量求导之定义法https://www.cnblogs.com/pinard/p/10773942.html
机器学习中的矩阵向量求导(三) 矩阵向量求导之微分法https://www.cnblogs.com/pinard/p/10791506.html
机器学习中的矩阵向量求导(四) 矩阵向量求导链式法则https://www.cnblogs.com/pinard/p/10825264.html
机器学习中的矩阵向量求导(五) 矩阵对矩阵的求导https://www.cnblogs.com/pinard/p/10930902.html
https://github.com/chenfork/machinelearning#机器学习集成学习文章与代码
集成学习原理小结https://www.cnblogs.com/pinard/p/6131423.html
集成学习之Adaboost算法原理小结https://www.cnblogs.com/pinard/p/6133937.html
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
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
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
XGBoost算法原理小结https://www.cnblogs.com/pinard/p/10979808.html
XGBoost类库使用小结https://www.cnblogs.com/pinard/p/11114748.html
代码https://github.com/ljpzzz/machinelearning/blob/master/ensemble-learning/xgboost-example.ipynb
https://github.com/chenfork/machinelearning#机器学习聚类算法文章与代码
K-Means聚类算法原理https://www.cnblogs.com/pinard/p/6164214.html
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
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
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/6221564.html
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
https://github.com/chenfork/machinelearning#机器学习降维算法文章与代码
主成分分析（PCA）原理总结https://www.cnblogs.com/pinard/p/6239403.html
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
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
局部线性嵌入(LLE)原理总结https://www.cnblogs.com/pinard/p/6266408.html
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
https://github.com/chenfork/machinelearning#机器学习关联算法文章与代码
典型关联分析(CCA)原理总结https://www.cnblogs.com/pinard/p/6288716.html
Apriori算法原理总结https://www.cnblogs.com/pinard/p/6293298.html
FP Tree算法原理总结https://www.cnblogs.com/pinard/p/6307064.html
PrefixSpan算法原理总结https://www.cnblogs.com/pinard/p/6323182.html
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
https://github.com/chenfork/machinelearning#机器学习推荐算法文章与代码
协同过滤推荐算法总结https://www.cnblogs.com/pinard/p/6349233.html
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
SimRank协同过滤推荐算法https://www.cnblogs.com/pinard/p/6362647.html
用Spark学习矩阵分解推荐算法https://www.cnblogs.com/pinard/p/6364932.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
贝叶斯个性化排序(BPR)算法小结https://www.cnblogs.com/pinard/p/9128682.html
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
https://github.com/chenfork/machinelearning#深度学习算法文章与代码
深度神经网络（DNN）模型与前向传播算法https://www.cnblogs.com/pinard/p/6418668.html
深度神经网络（DNN）反向传播算法(BP)https://www.cnblogs.com/pinard/p/6422831.html
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/6437495.html
深度神经网络（DNN）的正则化https://www.cnblogs.com/pinard/p/6472666.html
卷积神经网络(CNN)模型结构https://www.cnblogs.com/pinard/p/6483207.html
卷积神经网络(CNN)前向传播算法https://www.cnblogs.com/pinard/p/6489633.html
卷积神经网络(CNN)反向传播算法https://www.cnblogs.com/pinard/p/6494810.html
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
LSTM模型与前向反向传播算法https://www.cnblogs.com/pinard/p/6519110.html
受限玻尔兹曼机（RBM）原理总结https://www.cnblogs.com/pinard/p/6530523.html
https://github.com/chenfork/machinelearning#自然语言处理文章与代码
文本挖掘的分词原理https://www.cnblogs.com/pinard/p/6677078.html
文本挖掘预处理之向量化与Hash Trickhttps://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-IDFhttps://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
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
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
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
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
隐马尔科夫模型HMM（一）HMM模型https://www.cnblogs.com/pinard/p/6945257.html
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
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
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
用hmmlearn学习隐马尔科夫模型HMMhttps://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
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
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
word2vec原理(一) CBOW与Skip-Gram模型基础https://www.cnblogs.com/pinard/p/7160330.html
word2vec原理(二) 基于Hierarchical Softmax的模型https://www.cnblogs.com/pinard/p/7243513.html
word2vec原理(三) 基于Negative Sampling的模型https://www.cnblogs.com/pinard/p/7249903.html
用gensim学习word2vechttps://www.cnblogs.com/pinard/p/7278324.html
代码https://github.com/ljpzzz/machinelearning/blob/master/natural-language-processing/word2vec.ipynb
https://github.com/chenfork/machinelearning#特征工程与算法落地文章与代码
特征工程之特征选择https://www.cnblogs.com/pinard/p/9032759.html
特征工程之特征表达https://www.cnblogs.com/pinard/p/9061549.html
特征工程之特征预处理https://www.cnblogs.com/pinard/p/9093890.html
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
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