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https://github.com/apachecn/.github/tree/dev/docs/tree#统计机器学习
https://github.com/apachecn/.github/tree/dev/docs/tree#基础知识
AILearning 第1章_基础知识https://github.com/apachecn/AiLearning/blob/master/docs/ml/1.%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%9F%BA%E7%A1%80.md
CS229 中文笔记 一、引言http://ai-start.com/ml2014/html/week1.html
CS229 中文笔记 三、线性代数回顾http://ai-start.com/ml2014/html/week1.html
机器学习基石 1 -- The Learning Problemhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/2.md
机器学习基石 2 -- Learning to Answer Yes/Nohttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/3.md
机器学习基石 3 -- Types of Learninghttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/4.md
机器学习基石 4 -- Feasibility of Learninghttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/5.md
机器学习基石 6 -- Theory of Generalizationhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/7.md
机器学习基石 7 -- The VC Dimensionhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/8.md
机器学习基石 8 -- Noise and Errorhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/9.md
机器学习基石 16 -- Three Learning Principleshttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/17.md
写给人类的机器学习 一、为什么机器学习重要https://github.com/apachecn/ml-for-humans-zh/blob/master/1.md
SciPyCon 2018 sklearn 教程 一、Python 机器学习简介https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/1.md
SciPyCon 2018 sklearn 教程 二、Python 中的科学计算工具https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/2.md
SciPyCon 2018 sklearn 教程 九、sklearn 估计器接口回顾https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/9.md
SciPyCon 2018 sklearn 教程 十五、估计器流水线https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/15.md
数据科学和人工智能技术笔记 一、向量、矩阵和数组https://github.com/apachecn/ds-ai-tech-notes/blob/master/1.md
Sklearn 学习指南 第一章：机器学习 - 温和的介绍https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch01.md
https://github.com/apachecn/.github/tree/dev/docs/tree#线性回归逻辑回归softmax-回归
AILearning 第5章_逻辑回归https://github.com/apachecn/AiLearning/blob/master/docs/ml/5.Logistic%E5%9B%9E%E5%BD%92.md
AILearning 第8章_回归https://github.com/apachecn/AiLearning/blob/master/docs/ml/8.%E5%9B%9E%E5%BD%92.md
CS229 中文笔记 二、单变量线性回归http://ai-start.com/ml2014/html/week1.html
CS229 中文笔记 四、多变量线性回归http://ai-start.com/ml2014/html/week2.html
CS229 中文笔记 六、逻辑回归http://ai-start.com/ml2014/html/week2.html
DLAI 深度学习笔记 第一门课 第二周：神经网络的编程基础http://ai-start.com/dl2017/html/lesson1-week2.html
机器学习基石 9 -- Linear Regressionhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/10.md
机器学习基石 10 -- Logistic Regressionhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/11.md
机器学习基石 11 -- Linear Models for Classificationhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/12.md
机器学习基石 12 -- Nonlinear Transformationhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/13.md
机器学习技法 5 -- Kernel Logistic Regressionhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/23.md
Scikit-learn 秘籍 第二章 处理线性模型https://github.com/apachecn/sklearn-cookbook-zh/blob/master/2.md
Scikit-learn 秘籍 第四章 使用 scikit-learn 对数据分类https://github.com/apachecn/sklearn-cookbook-zh/blob/master/4.md
PythonProgramming.net 系列教程 第一部分 回归https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-ml/1.md
写给人类的机器学习 2.1 监督学习https://github.com/apachecn/ml-for-humans-zh/blob/master/2.1.md
写给人类的机器学习 2.2 监督学习 IIhttps://github.com/apachecn/ml-for-humans-zh/blob/master/2.2.md
Python 数据分析与挖掘实战 第5章　挖掘建模https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/5.md
Python 数据分析与挖掘实战 第13章　财政收入影响因素分析及预测模型https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/13.md
与 TensorFlow 的初次接触 2. TensorFlow 中的线性回归https://github.com/apachecn/misc-docs-zh/blob/master/docs/first_contact_with_tensorFlow/2.md
SciPyCon 2018 sklearn 教程 五、监督学习第一部分：分类https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/5.md
SciPyCon 2018 sklearn 教程 六、监督学习第二部分：回归分析https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/6.md
SciPyCon 2018 sklearn 教程 十七、深入：线性模型https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/17.md
数据科学和人工智能技术笔记 十一、线性回归https://github.com/apachecn/ds-ai-tech-notes/blob/master/11.md
数据科学和人工智能技术笔记 十二、逻辑回归https://github.com/apachecn/ds-ai-tech-notes/blob/master/12.md
Sklearn 学习指南 第二章：监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch02.md
https://github.com/apachecn/.github/tree/dev/docs/tree#决策树随机森林
AILearning 第3章_决策树算法https://github.com/apachecn/AiLearning/blob/master/docs/ml/3.%E5%86%B3%E7%AD%96%E6%A0%91.md
AILearning 第9章_树回归https://github.com/apachecn/AiLearning/blob/master/docs/ml/9.%E6%A0%91%E5%9B%9E%E5%BD%92.md
机器学习技法 9 -- Decision Treehttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/27.md
机器学习技法 10 -- Random Foresthttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/28.md
Scikit-learn 秘籍 第四章 使用 scikit-learn 对数据分类https://github.com/apachecn/sklearn-cookbook-zh/blob/master/4.md
写给人类的机器学习 2.3 监督学习 IIIhttps://github.com/apachecn/ml-for-humans-zh/blob/master/2.3.md
Python 数据分析与挖掘实战 第5章　挖掘建模https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/5.md
Python 数据分析与挖掘实战 第6章　电力窃漏电用户自动识别https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/6.md
SciPyCon 2018 sklearn 教程 十八、深入：决策树与森林https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/18.md
数据科学和人工智能技术笔记 十三、树和森林https://github.com/apachecn/ds-ai-tech-notes/blob/master/13.md
Sklearn 学习指南 第二章：监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch02.md
https://github.com/apachecn/.github/tree/dev/docs/tree#gdbtxgboost
机器学习技法 11 -- Gradient Boosted Decision Treehttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/29.md
https://github.com/apachecn/.github/tree/dev/docs/tree#朴素贝叶斯
AILearning 第4章_朴素贝叶斯https://github.com/apachecn/AiLearning/blob/master/docs/ml/4.%E6%9C%B4%E7%B4%A0%E8%B4%9D%E5%8F%B6%E6%96%AF.md
Scikit-learn 秘籍 第四章 使用 scikit-learn 对数据分类https://github.com/apachecn/sklearn-cookbook-zh/blob/master/4.md
数据科学和人工智能技术笔记 十六、朴素贝叶斯https://github.com/apachecn/ds-ai-tech-notes/blob/master/16.md
Sklearn 学习指南 第二章：监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch02.md
https://github.com/apachecn/.github/tree/dev/docs/tree#支持向量机
AILearning 第6章_支持向量机https://github.com/apachecn/AiLearning/blob/master/docs/ml/6.%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA.md
AILearning 支持向量机的几个通俗理解https://github.com/apachecn/AiLearning/blob/master/docs/ml/6.1.%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA%E7%9A%84%E5%87%A0%E4%B8%AA%E9%80%9A%E4%BF%97%E7%90%86%E8%A7%A3.md
CS229 中文笔记 十二、支持向量机http://ai-start.com/ml2014/html/week7.html
机器学习技法 1 -- Linear Support Vector Machinehttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/19.md
机器学习技法 2 -- Dual Support Vector Machinehttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/20.md
机器学习技法 3 -- Kernel Support Vector Machinehttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/21.md
机器学习技法 4 -- Soft-Margin Support Vector Machinehttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/22.md
机器学习技法 6 -- Support Vector Regressionhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/24.md
Scikit-learn 秘籍 第四章 使用 scikit-learn 对数据分类https://github.com/apachecn/sklearn-cookbook-zh/blob/master/4.md
PythonProgramming.net 系列教程 第二部分 分类https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-ml/2.md
写给人类的机器学习 2.2 监督学习 IIhttps://github.com/apachecn/ml-for-humans-zh/blob/master/2.2.md
Python 数据分析与挖掘实战 第9章　基于水色图像的水质评价https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/9.md
数据科学和人工智能技术笔记 十五、支持向量机https://github.com/apachecn/ds-ai-tech-notes/blob/master/15.md
Sklearn 学习指南 第二章：监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch02.md
https://github.com/apachecn/.github/tree/dev/docs/tree#k-近邻
AILearning 第2章_K近邻算法https://github.com/apachecn/AiLearning/blob/master/docs/ml/2.k-%E8%BF%91%E9%82%BB%E7%AE%97%E6%B3%95.md
Scikit-learn 秘籍 第三章 使用距离向量构建模型https://github.com/apachecn/sklearn-cookbook-zh/blob/master/3.md
PythonProgramming.net 系列教程 第二部分 分类https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-ml/2.md
写给人类的机器学习 2.3 监督学习 IIIhttps://github.com/apachecn/ml-for-humans-zh/blob/master/2.3.md
SciPyCon 2018 sklearn 教程 五、监督学习第一部分：分类https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/5.md
SciPyCon 2018 sklearn 教程 六、监督学习第二部分：回归分析https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/6.md
数据科学和人工智能技术笔记 十四、K 最近邻https://github.com/apachecn/ds-ai-tech-notes/blob/master/14.md
https://github.com/apachecn/.github/tree/dev/docs/tree#kmeans
AILearning 第10章_KMeans聚类https://github.com/apachecn/AiLearning/blob/master/docs/ml/10.k-means%E8%81%9A%E7%B1%BB.md
CS229 中文笔记 十三、聚类http://ai-start.com/ml2014/html/week8.html
Scikit-learn 秘籍 第三章 使用距离向量构建模型https://github.com/apachecn/sklearn-cookbook-zh/blob/master/3.md
PythonProgramming.net 系列教程 第三部分 聚类https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-ml/3.md
写给人类的机器学习 三、无监督学习https://github.com/apachecn/ml-for-humans-zh/blob/master/3.md
Python 数据分析与挖掘实战 第5章　挖掘建模https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/5.md
Python 数据分析与挖掘实战 第7章　航空公司客户价值分析https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/7.md
Python 数据分析与挖掘实战 第8章　中医证型关联规则挖掘https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/8.md
与 TensorFlow 的初次接触 3. TensorFlow 中的聚类https://github.com/apachecn/misc-docs-zh/blob/master/docs/first_contact_with_tensorFlow/3.md
SciPyCon 2018 sklearn 教程 八、无监督学习第二部分：聚类https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/8.md
TensorFlow 学习指南 三、学习https://github.com/apachecn/learning-tf-zh/blob/master/3.md
数据科学和人工智能技术笔记 十七、聚类https://github.com/apachecn/ds-ai-tech-notes/blob/master/17.md
Sklearn 学习指南 第三章：无监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch03.md
https://github.com/apachecn/.github/tree/dev/docs/tree#均值移动
PythonProgramming.net 系列教程 第三部分 聚类https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-ml/3.md
数据科学和人工智能技术笔记 十七、聚类https://github.com/apachecn/ds-ai-tech-notes/blob/master/17.md
Sklearn 学习指南 第三章：无监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch03.md
https://github.com/apachecn/.github/tree/dev/docs/tree#层次聚类
写给人类的机器学习 三、无监督学习https://github.com/apachecn/ml-for-humans-zh/blob/master/3.md
Python 数据分析与挖掘实战 第14章　基于基站定位数据的商圈分析https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/14.md
SciPyCon 2018 sklearn 教程 二十、无监督学习：层次和基于密度的聚类算法https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/20.md
数据科学和人工智能技术笔记 十七、聚类https://github.com/apachecn/ds-ai-tech-notes/blob/master/17.md
Sklearn 学习指南 第三章：无监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch03.md
https://github.com/apachecn/.github/tree/dev/docs/tree#dbscan
SciPyCon 2018 sklearn 教程 二十、无监督学习：层次和基于密度的聚类算法https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/20.md
数据科学和人工智能技术笔记 十七、聚类https://github.com/apachecn/ds-ai-tech-notes/blob/master/17.md
https://github.com/apachecn/.github/tree/dev/docs/tree#高斯混合
Scikit-learn 秘籍 第三章 使用距离向量构建模型https://github.com/apachecn/sklearn-cookbook-zh/blob/master/3.md
Sklearn 学习指南 第三章：无监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch03.md
https://github.com/apachecn/.github/tree/dev/docs/tree#boostingbaggingblending
机器学习技法 7 -- Blending and Bagginghttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/25.md
https://github.com/apachecn/.github/tree/dev/docs/tree#adaboost
AILearning 第7章_集成方法https://github.com/apachecn/AiLearning/blob/master/docs/ml/7.%E9%9B%86%E6%88%90%E6%96%B9%E6%B3%95-%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97%E5%92%8CAdaBoost.md
机器学习技法 8 -- Adaptive Boostinghttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/26.md
https://github.com/apachecn/.github/tree/dev/docs/tree#pca
AILearning 第13章_PCA降维https://github.com/apachecn/AiLearning/blob/master/docs/ml/13.%E5%88%A9%E7%94%A8PCA%E6%9D%A5%E7%AE%80%E5%8C%96%E6%95%B0%E6%8D%AE.md
AILearning 第14章_SVD简化数据https://github.com/apachecn/AiLearning/blob/master/docs/ml/14.%E5%88%A9%E7%94%A8SVD%E7%AE%80%E5%8C%96%E6%95%B0%E6%8D%AE.md
CS229 中文笔记 十四、降维http://ai-start.com/ml2014/html/week8.html
写给人类的机器学习 三、无监督学习https://github.com/apachecn/ml-for-humans-zh/blob/master/3.md
SciPyCon 2018 sklearn 教程 七、无监督学习第一部分：变换https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/7.md
Sklearn 学习指南 第三章：无监督学习https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch03.md
https://github.com/apachecn/.github/tree/dev/docs/tree#lda
Scikit-learn 秘籍 第四章 使用 scikit-learn 对数据分类https://github.com/apachecn/sklearn-cookbook-zh/blob/master/4.md
https://github.com/apachecn/.github/tree/dev/docs/tree#流形学习
SciPyCon 2018 sklearn 教程 二十一、无监督学习：非线性降维https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/21.md
https://github.com/apachecn/.github/tree/dev/docs/tree#异常检测
CS229 中文笔记 十五、异常检测http://ai-start.com/ml2014/html/week9.html
SciPyCon 2018 sklearn 教程 二十二、无监督学习：异常检测https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/22.md
https://github.com/apachecn/.github/tree/dev/docs/tree#apriorifp-growth
AILearning 第11章_Apriori算法https://github.com/apachecn/AiLearning/blob/master/docs/ml/11.%E4%BD%BF%E7%94%A8Apriori%E7%AE%97%E6%B3%95%E8%BF%9B%E8%A1%8C%E5%85%B3%E8%81%94%E5%88%86%E6%9E%90.md
AILearning 第12章_FP-growth算法https://github.com/apachecn/AiLearning/blob/master/docs/ml/12.%E4%BD%BF%E7%94%A8FP-growth%E7%AE%97%E6%B3%95%E6%9D%A5%E9%AB%98%E6%95%88%E5%8F%91%E7%8E%B0%E9%A2%91%E7%B9%81%E9%A1%B9%E9%9B%86.md
Python 数据分析与挖掘实战 第5章　挖掘建模https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/5.md
Python 数据分析与挖掘实战 第8章　中医证型关联规则挖掘https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/8.md
https://github.com/apachecn/.github/tree/dev/docs/tree#深度学习
https://github.com/apachecn/.github/tree/dev/docs/tree#基础知识-1
DLAI 深度学习笔记 第一门课 第一周：深度学习引言http://ai-start.com/dl2017/html/lesson1-week1.html
与 TensorFlow 的初次接触 1. TensorFlow 基础知识https://github.com/apachecn/misc-docs-zh/blob/master/docs/first_contact_with_tensorFlow/1.md
TensorFlow 学习指南 一、基础https://github.com/apachecn/learning-tf-zh/blob/master/1.md
https://github.com/apachecn/.github/tree/dev/docs/tree#mlp
CS229 中文笔记 八、神经网络：表述http://ai-start.com/ml2014/html/week4.html
CS229 中文笔记 九、神经网络的学习http://ai-start.com/ml2014/html/week5.html
DLAI 深度学习笔记 第一门课 第三周：浅层神经网络http://ai-start.com/dl2017/html/lesson1-week3.html
DLAI 深度学习笔记 第一门课 第四周：深层神经网络http://ai-start.com/dl2017/html/lesson1-week4.html
机器学习技法 12 -- Neural Networkhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/30.md
机器学习技法 13 -- Deep Learninghttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/31.md
机器学习技法 14 -- Radial Basis Function Networkhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/32.md
PythonProgramming.net 系列教程 第四部分 神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-ml/4.md
写给人类的机器学习 四、神经网络和深度学习https://github.com/apachecn/ml-for-humans-zh/blob/master/4.md
Python 数据分析与挖掘实战 第5章　挖掘建模https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/5.md
Python 数据分析与挖掘实战 第6章　电力窃漏电用户自动识别https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/6.md
Python 数据分析与挖掘实战 第10章　家用电器用户行为分析与事件识别https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/10.md
Python 数据分析与挖掘实战 第13章　财政收入影响因素分析及预测模型https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/13.md
与 TensorFlow 的初次接触 4. TensorFlow 中的单层神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/first_contact_with_tensorFlow/4.md
与 TensorFlow 的初次接触 5. TensorFlow 中的多层神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/first_contact_with_tensorFlow/5.md
TensorFlow Rager 教程 一、如何使用 TensorFlow Eager 构建简单的神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/1.md
数据科学和人工智能技术笔记 十八、Kerashttps://github.com/apachecn/ds-ai-tech-notes/blob/master/18.md
https://github.com/apachecn/.github/tree/dev/docs/tree#cnn
DLAI 深度学习笔记 第四门课 第一周 卷积神经网络http://ai-start.com/dl2017/html/lesson4-week1.html
DLAI 深度学习笔记 第四门课 第二周 深度卷积网络：实例探究http://ai-start.com/dl2017/html/lesson4-week2.html
TensorFlow Rager 教程 七、使用 TensorFlow Eager 构建用于情感识别的卷积神经网络（CNN）https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/7.md
https://github.com/apachecn/.github/tree/dev/docs/tree#rnn
DLAI 深度学习笔记 第五门课 第一周 循环序列模型http://ai-start.com/dl2017/html/lesson5-week1.html
DLAI 深度学习笔记 第五门课 第三周 序列模型和注意力机制http://ai-start.com/dl2017/html/lesson5-week3.html
TensorFlow Rager 教程 八、用于 TensorFlow Eager 序列分类的动态循坏神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/8.md
TensorFlow Rager 教程 九、用于 TensorFlow Eager 时间序列回归的递归神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/9.md
https://github.com/apachecn/.github/tree/dev/docs/tree#时间序列
第5章　挖掘建模https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/5.md
Python 数据分析与挖掘实战 第11章　应用系统负载分析与磁盘容量预测https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/11.md
TensorFlow Rager 教程 九、用于 TensorFlow Eager 时间序列回归的递归神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/9.md
https://github.com/apachecn/.github/tree/dev/docs/tree#机器视觉
CS229 中文笔记 十八、应用实例：图片文字识别http://ai-start.com/ml2014/html/week10.html
DLAI 深度学习笔记 第四门课 第三周 目标检测http://ai-start.com/dl2017/html/lesson4-week3.html
DLAI 深度学习笔记 第四门课 第四周 特殊应用：人脸识别和神经风格转换http://ai-start.com/dl2017/html/lesson4-week4.html
PythonProgramming.net 系列教程 图像和视频分析https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-opencv.md
PythonProgramming.net 系列教程 TensorFlow 目标检测https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-tf-object-detection.md
数据科学和人工智能技术笔记 四、图像预处理https://github.com/apachecn/ds-ai-tech-notes/blob/master/4.md
https://github.com/apachecn/.github/tree/dev/docs/tree#图嵌入图的表示学习
图嵌入综述：问题，技术与应用 第一、二章https://github.com/apachecn/misc-docs-zh/blob/master/docs/ge-survey-arxiv-1709-07604-zh/1.md
图嵌入综述：问题，技术与应用 第三章https://github.com/apachecn/misc-docs-zh/blob/master/docs/ge-survey-arxiv-1709-07604-zh/2.md
图嵌入综述：问题，技术与应用 4.1 ~ 4.2https://github.com/apachecn/misc-docs-zh/blob/master/docs/ge-survey-arxiv-1709-07604-zh/3.md
图嵌入综述：问题，技术与应用 4.3 ~ 4.7https://github.com/apachecn/misc-docs-zh/blob/master/docs/ge-survey-arxiv-1709-07604-zh/4.md
图嵌入综述：问题，技术与应用 第五、六、七章https://github.com/apachecn/misc-docs-zh/blob/master/docs/ge-survey-arxiv-1709-07604-zh/5.md
https://github.com/apachecn/.github/tree/dev/docs/tree#自然语言处理
DLAI 深度学习笔记 第五门课 第二周 自然语言处理与词嵌入http://ai-start.com/dl2017/html/lesson5-week2.html
PythonProgramming.net 系列教程 自然语言处理教程https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-nltk.md
PythonProgramming.net 系列教程 TensorFlow 聊天机器人https://github.com/apachecn/misc-docs-zh/blob/master/docs/python-programming-net/python-programming-net-tf-chatbot.md
Python 数据分析与挖掘实战 第15章　电商产品评论数据情感分析https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/15.md
TensorFlow Rager 教程 七、使用 TensorFlow Eager 构建用于情感识别的卷积神经网络（CNN）https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/7.md
TensorFlow Rager 教程 八、用于 TensorFlow Eager 序列分类的动态循坏神经网络https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/8.md
SciPyCon 2018 sklearn 教程 十一、文本特征提取https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/11.md
SciPyCon 2018 sklearn 教程 十二、案例学习：用于 SMS 垃圾检测的文本分类https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/12.md
SciPyCon 2018 sklearn 教程 二十三、核外学习 - 用于语义分析的大规模文本分类https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/23.md
数据科学和人工智能技术笔记 五、文本预处理https://github.com/apachecn/ds-ai-tech-notes/blob/master/5.md
https://github.com/apachecn/.github/tree/dev/docs/tree#强化学习
写给人类的机器学习 五、强化学习https://github.com/apachecn/ml-for-humans-zh/blob/master/5.md
https://github.com/apachecn/.github/tree/dev/docs/tree#推荐系统
AILearning 第16章_推荐系统https://github.com/apachecn/AiLearning/blob/master/docs/ml/16.%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F.md
CS229 中文笔记 十六、推荐系统http://ai-start.com/ml2014/html/week9.html
机器学习技法 15 -- Matrix Factorizationhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/33.md
Python 数据分析与挖掘实战 第12章　电子商务网站用户行为分析及服务推荐https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/12.md
基于深度学习的推荐系统：综述和新视角 第一、二章https://github.com/apachecn/misc-docs-zh/blob/master/docs/rs-survey-arxiv-1707-07435-zh/1.md
基于深度学习的推荐系统：综述和新视角 3.1 ~ 3.3https://github.com/apachecn/misc-docs-zh/blob/master/docs/rs-survey-arxiv-1707-07435-zh/2.md
基于深度学习的推荐系统：综述和新视角 3.4 ~ 3.11https://github.com/apachecn/misc-docs-zh/blob/master/docs/rs-survey-arxiv-1707-07435-zh/3.md
https://github.com/apachecn/.github/tree/dev/docs/tree#预处理特征工程
Scikit-learn 秘籍 第一章 模型预处理https://github.com/apachecn/sklearn-cookbook-zh/blob/master/1.md
Python 数据分析与挖掘实战 第3章　数据探索https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/3.md
Python 数据分析与挖掘实战 第4章　数据预处理https://github.com/apachecn/python_data_analysis_and_mining_action/blob/gitbook/4.md
TensorFlow Rager 教程 四、文本序列到 TFRecordshttps://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/4.md
TensorFlow Rager 教程 五、如何将原始图片数据转换为 TFRecordshttps://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/5.md
TensorFlow Rager 教程 六、如何使用 TensorFlow Eager 从 TFRecords 批量读取数据https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/6.md
SciPyCon 2018 sklearn 教程 三、数据表示和可视化https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/3.md
SciPyCon 2018 sklearn 教程 七、无监督学习第一部分：变换https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/7.md
SciPyCon 2018 sklearn 教程 十、案例学习：泰坦尼克幸存者https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/10.md
SciPyCon 2018 sklearn 教程 十一、文本特征提取https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/11.md
SciPyCon 2018 sklearn 教程 十九、自动特征选择https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/19.md
数据科学和人工智能技术笔记 二、数据准备https://github.com/apachecn/ds-ai-tech-notes/blob/master/2.md
数据科学和人工智能技术笔记 三、数据预处理https://github.com/apachecn/ds-ai-tech-notes/blob/master/3.md
数据科学和人工智能技术笔记 四、图像预处理https://github.com/apachecn/ds-ai-tech-notes/blob/master/4.md
数据科学和人工智能技术笔记 五、文本预处理https://github.com/apachecn/ds-ai-tech-notes/blob/master/5.md
数据科学和人工智能技术笔记 六、日期时间预处理https://github.com/apachecn/ds-ai-tech-notes/blob/master/6.md
数据科学和人工智能技术笔记 七、特征工程https://github.com/apachecn/ds-ai-tech-notes/blob/master/7.md
数据科学和人工智能技术笔记 八、特征选择https://github.com/apachecn/ds-ai-tech-notes/blob/master/8.md
数据科学和人工智能技术笔记 十九、数据整理（上）https://github.com/apachecn/ds-ai-tech-notes/blob/master/19.1.md
数据科学和人工智能技术笔记 十九、数据整理（下）https://github.com/apachecn/ds-ai-tech-notes/blob/master/19.2.md
数据科学和人工智能技术笔记 二十、数据可视化https://github.com/apachecn/ds-ai-tech-notes/blob/master/20.md
Sklearn 学习指南 第四章：高级功能https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch04.md
https://github.com/apachecn/.github/tree/dev/docs/tree#模型评估模型调优
CS229 中文笔记 七、正则化http://ai-start.com/ml2014/html/week2.html
CS229 中文笔记 十、应用机器学习的建议http://ai-start.com/ml2014/html/week6.html
CS229 中文笔记 十一、机器学习系统的设计http://ai-start.com/ml2014/html/week6.html
DLAI 深度学习笔记 第二门课 第一周：深度学习的实用层面http://ai-start.com/dl2017/html/lesson2-week1.html
DLAI 深度学习笔记 第二门课 第三周超参数调试，batch正则化和程序框架http://ai-start.com/dl2017/html/lesson2-week3.html
DLAI 深度学习笔记 第三门课 第一周：机器学习策略（1）http://ai-start.com/dl2017/html/lesson3-week1.html
DLAI 深度学习笔记 第三门课 第二周：机器学习策略（2）http://ai-start.com/dl2017/html/lesson3-week2.html
机器学习基石 5 -- Training versus Testinghttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/6.md
机器学习基石 13 -- Hazard of Overfittinghttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/14.md
机器学习基石 14 -- Regularizationhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/15.md
机器学习基石 15 -- Validationhttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/16.md
Scikit-learn 秘籍 第二章 处理线性模型https://github.com/apachecn/sklearn-cookbook-zh/blob/master/2.md
Scikit-learn 秘籍 第五章 模型后处理https://github.com/apachecn/sklearn-cookbook-zh/blob/master/5.md
TensorFlow Rager 教程 二、在 Eager 模式中使用指标https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/2.md
SciPyCon 2018 sklearn 教程 四、训练和测试数据https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/4.md
SciPyCon 2018 sklearn 教程 十三、交叉验证和得分方法https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/13.md
SciPyCon 2018 sklearn 教程 十四、参数选择、验证和测试https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/14.md
SciPyCon 2018 sklearn 教程 十六、模型评估、得分指标和处理不平衡类别https://github.com/apachecn/scipycon-2018-sklearn-tut-zh/blob/master/16.md
TensorFlow 学习指南 二、线性模型https://github.com/apachecn/learning-tf-zh/blob/master/2.md
数据科学和人工智能技术笔记 九、模型验证https://github.com/apachecn/ds-ai-tech-notes/blob/master/9.md
数据科学和人工智能技术笔记 十、模型选择https://github.com/apachecn/ds-ai-tech-notes/blob/master/10.md
Sklearn 学习指南 第四章：高级功能https://github.com/apachecn/misc-docs-zh/blob/master/docs/learning-sklearn/ch04.md
https://github.com/apachecn/.github/tree/dev/docs/tree#最优化
https://github.com/apachecn/.github/tree/dev/docs/tree#梯度下降
CS229 中文笔记 十七、大规模机器学习http://ai-start.com/ml2014/html/week10.html
DLAI 深度学习笔记 第一门课 第二周：优化算法http://ai-start.com/dl2017/html/lesson2-week2.html
https://github.com/apachecn/.github/tree/dev/docs/tree#其它
机器学习技法 16（完结） -- Finalehttps://github.com/apachecn/ntu-hsuantienlin-ml/blob/master/34.md
CS229 中文笔记 十九、总结http://ai-start.com/ml2014/html/week10.html
写给人类的机器学习 六、最好的机器学习资源https://github.com/apachecn/ml-for-humans-zh/blob/master/6.md
与 TensorFlow 的初次接触 6. 并行https://github.com/apachecn/misc-docs-zh/blob/master/docs/first_contact_with_tensorFlow/6.md
TensorFlow Rager 教程 三、如何保存和恢复训练模型https://github.com/apachecn/misc-docs-zh/blob/master/docs/tf-eager-tut/3.md
TensorFlow 学习指南 四、分布式https://github.com/apachecn/learning-tf-zh/blob/master/4.md
数据科学和人工智能技术笔记 二十一、统计学https://github.com/apachecn/ds-ai-tech-notes/blob/master/21.md
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