# 目录

## 目录

* [封面](master)
* [目录](summary)

[机器学习算法地图](http://www.tensorinfinity.com/paper_18.html)

![machine-learning-map](https://3298324061-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LpO5sn2FY1C9esHFJmo%2F-LpO5t6XJGnSXrbsZ_UC%2F-LpO5wULGc9TIxxqTTC8%2Fmachine-learning-map.png?generation=1569158070399942\&alt=media)

## 前言

* [前言](perface)
* [个人前言](personal-perface)
* [机器学习前言](machine-learning-perface)
  * [什么是机器学习和模式识别](../machine-learning-perface#什么是机器学习和模式识别)
  * [机器学习的应用](../machine-learning-perface#机器学习的应用)
  * [机器学习的流程](../machine-learning#机器学习的流程)
  * [不同机器学习算法预测效果不同](../machine-learning-perface#不同机器学习算法预测效果不同)
  * [快速入门机器学习](../machine-learning-perface#快速入门机器学习)
  * [机器学习需要参考哪些书](../machine-learning-perface#机器学习需要参考哪些书)
  * [机器学习的学习路径](../machine-learning-perface#机器学习的学习路径)
  * [深度学习的学习路径](../machine-learning-perface#深度学习的学习路径)
  * [互联网机器学习特定岗位所需技能](../machine-learning-perface#互联网机器学习特定岗位所需技能)
* [机器学习面试](interview)

## 数学基础

* 数学基础
* [微积分](calculus)
  * [泰勒展开](calculus/taylor-expansion)
  * [e的直观认识](calculus/intuition-of-e)
  * [傅里叶变换](calculus/fourier-transform)
  * [希尔伯特空间](calculus/hilbert-space)
* [线性代数](linear-algebra)
  * [范数](linear-algebra/norm)
  * [矩阵求导](linear-algebra/matrix-derivative)
  * [特征值](linear-algebra/eigenvalue)
  * [SVD奇异值分解](linear-algebra/singular-value-decomposition)
* [概率与信息论](statistics-and-information-theory)
  * [综述概率论基本定义](statistics-and-information-theory/review-of-statistics)
  * [概率论与贝叶斯先验](statistics-and-information-theory/probability-and-bayesian-prior)
  * [正态分布](statistics-and-information-theory/normal-distribution)
  * [贝叶斯概率](statistics-and-information-theory/bayes-probability)
  * [概率符号说明](statistics-and-information-theory/probability-symbol-explaination)
  * [共轭先验](statistics-and-information-theory/conjugate-prior)
  * [概率图模型](https://github.com/luweikxy/machine-learning-notes/tree/123475954b5254a8109117f87d962d5037609eec/content/mathematics/statistics-and-information-theory/probability-graphical-model.md)
  * [信息论](statistics-and-information-theory/information-theory)
* [数值计算与优化](numerical-calculation-and-optimization)
  * [最小二乘法](numerical-calculation-and-optimization/least-square-method)
  * [等式约束的拉格朗日乘子法](numerical-calculation-and-optimization/lagrangian-multiplier-method)
  * [凸优化](numerical-calculation-and-optimization/convex-optimization)
    * [凸集和凸函数](numerical-calculation-and-optimization/convex-optimization/convex-set-and-convex-function)
    * [凸优化问题](numerical-calculation-and-optimization/convex-optimization/convex-optimization-problem)
* [梯度下降算法](gradient-descent-algorithm)
  * [随机梯度下降SGD](gradient-descent-algorithm/sgd)
  * [动量法Momentum](gradient-descent-algorithm/momentum)
  * [牛顿动量Nesterov](gradient-descent-algorithm/nesterov)
  * [AdaGrad](gradient-descent-algorithm/adagrad)
  * [RMSprop](gradient-descent-algorithm/rmsprop)
  * [Adadelta](gradient-descent-algorithm/adadelta)
  * [Adam](gradient-descent-algorithm/adam)
  * [Nadam](gradient-descent-algorithm/nadam)
  * [AMSGrad](gradient-descent-algorithm/amsgrad)
  * [AdasMax](gradient-descent-algorithm/adamax)
* [概率图模型](probability-graphical-model)
  * [概率图模型概论](probability-graphical-model/probability-graphical-model-introduction)
  * [概率图简介](probability-graphical-model/probability-graph-introduction)

## 编程基础

* 编程基础
* [linux](linux)
  * [linux常用命令](linux/linux-command)
  * [shell](linux/shell)
    * [输入输出重定向](linux/shell/input_output_redirection)
* [python](python)
  * [python简介](python/introduction)
  * [python语法](python/grammar)
    * [基础语法](python/grammar/basis)
    * [数据结构](python/grammar/data-structure)
    * [过程控制](python/grammar/process-control)
    * [函数](python/grammar/function)
    * [类和对象](python/grammar/class)
    * [文件操作](python/grammar/file)
    * [正则表达式](python/grammar/regular-expression)
  * [python库](python/library)
    * [numpy](python/library/numpy)
    * [pandas](python/library/pandas)
    * [scipy](python/library/scipy)
    * [matplotlib](python/library/matplotlib)
    * [scikit-learn](python/library/scikit-learn)
  * [python应用](python/application)
    * [排序算法](python/application/sort)
* [数据结构与算法](data-structures-and-algorithms)
  * [数据结构](data-structures-and-algorithms/data-structures)
  * [算法思想](data-structures-and-algorithms/algorithms)
    * [排序](data-structures-and-algorithms/algorithms/sort)
      * [堆排序](data-structures-and-algorithms/algorithms/sort/heap-sort)
      * [归并排序](data-structures-and-algorithms/algorithms/sort/merge-sort)
      * [快速排序](data-structures-and-algorithms/algorithms/sort/quick-sort)
    * [递归](data-structures-and-algorithms/algorithms/recursion)
  * [剑指offer](data-structures-and-algorithms/jianzhi-offer)
    * [链表](data-structures-and-algorithms/jianzhi-offer/list)
    * [二叉树](data-structures-and-algorithms/jianzhi-offer/binary-tree)
    * [数组](data-structures-and-algorithms/jianzhi-offer/array)
    * [字符串](data-structures-and-algorithms/jianzhi-offer/string)
    * [栈和队列](data-structures-and-algorithms/jianzhi-offer/stack-and-queue)
    * [递归和回溯法](data-structures-and-algorithms/jianzhi-offer/recursion-and-back-tracking)
    * [动态规划](data-structures-and-algorithms/jianzhi-offer/dynamic-programming)
    * [其他](data-structures-and-algorithms/jianzhi-offer/others)
  * [leetcode](data-structures-and-algorithms/leetcode)
  * [编程语言](data-structures-and-algorithms/programming-language)
    * [c++](data-structures-and-algorithms/programming-language/c++)
* [Hadoop](hadoop)
  * [Hadoop简介](hadoop/hadoop-introduction)
* [MapReduce](hadoop/map-reduce)
* [Hive](hive)
* Spark
* [TensorFlow](tensorflow)
  * [TensorFlow1.0](tensorflow/tensorflow1.0)
    * [TensorFlow基础](tensorflow/tensorflow1.0/basis)
    * [TensorFlow基础概念解析](tensorflow/tensorflow1.0/basic-concept-analysis)
    * [TensorFlow机器学习基础](https://github.com/luweikxy/machine-learning-notes/tree/123475954b5254a8109117f87d962d5037609eec/content/coding/tensorflow/tensorflow1.0/machine-learning-foundation/machine-learning-foundation.md)
    * [Tensorflow分布式架构](tensorflow/tensorflow1.0/distributed-architecture)
  * [TensorFlow2.0](tensorflow/tensorflow2.0)
* [PyTorch](pytorch)

## 机器学习

* [机器学习](machine-learning)
* [机器学习概论](machine-learning-introduction)
* [特征工程](feature-engineering)
* [感知机](perceptron)
* [k近邻](k-nearest-neighbor)
* [朴素贝叶斯](naive-bayes)
* [线性模型](linear-model)
  * [最大熵模型](linear-model/maximum-entropy-model)
  * [指数族分布与广义线性模型](linear-model/exponential-family-distribution-and-generalized-linear-model)
  * [线性回归](../linear-model/linear-regression#线性回归)
    * [Ridge回归（岭回归）](../linear-model/linear-regression#Ridge回归（岭回归）)
    * [Lasso回归](../linear-model/linear-regression#Lasso回归)
  * [Logistic回归-对数几率回归](linear-model/logistic-regression)
* [决策树](decision-tree)
* [支持向量机](support-vector-machine)
  * [线性可分支持向量机与硬间隔最大化](support-vector-machine/linear-separable-svm)
  * [线性支持向量机与软间隔最大化](support-vector-machine/linear-svm)
  * [非线性支持向量机与核函数](support-vector-machine/nonlinear-svm-and-kernel-function)
  * [序列最小最优化算法SMO](support-vector-machine/smo)
  * [SVM总结](support-vector-machine/svm-summary)
* [集成学习](ensemble-learning)
  * Bagging
    * [随机森林](ensemble-learning/bagging/random-forest)
  * Boosting
    * [AdaBoost](ensemble-learning/boosting/adaboost)
    * GradientBoosting
      * [GBDT](ensemble-learning/boosting/gradientboosting/gbdt)
      * [XGBoost](ensemble-learning/boosting/gradientboosting/xgboost)
        * [XGBoost理论](ensemble-learning/boosting/gradientboosting/xgboost/xgboost-theory)
        * [XGBoost实践](ensemble-learning/boosting/gradientboosting/xgboost/xgboost-practice)
  * Stacking
* [降维](dimensionality-reduction)
  * [PCA主成分分析](dimensionality-reduction/principal-component-analysis)
  * [流形学习](dimensionality-reduction/manifold-learning)
* [EM算法](expectation-maximization-algorithm)
* [HMM隐马尔科夫模型](hidden-markov-model)
* [CRF条件随机场](conditional-random-field)
* [聚类](clustering)
  * [k均值聚类](clustering/k-means-clustering)
  * [高斯混合模型](clustering/gaussian-mixture-model)
* [主题模型](topic-model)
  * [LDA隐狄利克雷分布](topic-model/latent-dirichlet-allocation)
* [知识点](tips)
  * [损失函数](tips/loss-function)
  * [负采样](tips/negtive-sampling)
* [机器学习算法总结](machine-learning-algorithm-summary)

## 深度学习

* [深度学习](deep-learning)
* [深度学习概论](deep-learning-introduction)
* [ANN人工神经网络](artificial-neural-network)
* [知识点](tips-1)
  * [Batch Normalization](tips-1/batch-normalization)
* [CNN卷积神经网络](convolutional-neural-network)
* [深度学习优化算法](https://github.com/luweikxy/machine-learning-notes/tree/123475954b5254a8109117f87d962d5037609eec/content/deep-learning/deep-learning-optimization-algorithm/deep-learning-optimization-algorithm.md)
* [RNN循环神经网络](recurrent-neural-network)
* [LSTM长短期记忆网络](long-short-term-memory-networks)
* [GRU门控循环单元](gated-recurrent-unit)
* [GNN图神经网络](graph-neural-networks)
  * [GNN图神经网络综述](graph-neural-networks/graph-neural-networks-review)
  * [GCN图卷积网络](graph-neural-networks/graph-convolutional-networks)
    * [GCN图卷积网络初步理解](https://github.com/luweikxy/machine-learning-notes/tree/123475954b5254a8109117f87d962d5037609eec/content/deep-learning/graph-neural-networks/graph-convolutional-networks/gcn-preliminary-understand.md)
    * [GCN图卷积网络的numpy简单实现](https://github.com/luweikxy/machine-learning-notes/tree/123475954b5254a8109117f87d962d5037609eec/content/deep-learning/graph-neural-networks/graph-convolutional-networks/gcn-numpy-fulfillment.md)
    * [GCN图卷积网络本质理解](graph-neural-networks/graph-convolutional-networks/gcn-essential-understand)
    * [GCN图卷积网络全面理解](graph-neural-networks/graph-convolutional-networks/gcn-comprehensive-understand)
    * [SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS ICLR2017](graph-neural-networks/graph-convolutional-networks/semi-supervised-classification-with-graph-convolutional-networks)
* [神经网络架构搜索](neural-architecture-search)
  * [Weight-Agnostic-Neural-Networks Google2019](neural-architecture-search/weight-agnostic-neural-networks)

## 强化学习

* 强化学习
* [强化学习概论](reinforcement-learning-introduction)
* [马尔科夫决策过程](markov-decision-processes)
* [动态规划](dynamic-programming)
* [无模型方法一：蒙特卡洛](model-free-methods-1-monte-carlo)
* [无模型方法二：时间差分](model-free-methods-2-time-difference)
* [无模型方法三：多步自举](model-free-methods-3-multi-step-bootstrap)
* [函数近似和深度网络](function-approximation-and-deep-network)
* [策略梯度算法](policy-gradient-algorithm)
* [深度强化学习](deep-reinforcement-learning)
* [基于模型的强化学习](model-based-reinforcement-learning)
* [强化学习前景](reinforcement-learning-prospect)

## 自然语言处理

* [自然语言处理](natural-language-processing)
* [自然语言处理概论](natural-language-processing-introduction)
* [自然语言](natural-language)
* [语言模型和中文分词](language-model-and-chinese-word-segmentation)
* [word2vec](word2vec)
* [Seq2Seq模型和Attention机制](seq2seq-and-attention-mechanism)
* [Self-Attention和Transformer](self-attention-and-transformer)

## 知识图谱

* 知识图谱

## 推荐系统

* [推荐系统](recommender-systems)
* [推荐系统概述](recommender-systems-introduction)
* 基础知识
* [进阶知识](advanced-knowledge)
  * [机器学习](advanced-knowledge/machine-learning)
    * [Factorization Machines ICDM2010](advanced-knowledge/machine-learning/factorization-machines)
  * [embedding](advanced-knowledge/embedding)
    * Network Embedding
      * [LINE: Large-scale Information Network Embedding](advanced-knowledge/embedding/network-embedding/line-large-scale-information-network-embedding)
  * [深度学习](advanced-knowledge/deep-learning)
    * [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction 2017](advanced-knowledge/deep-learning/deepfm-a-factorization-machine-based-neural-network-for-ctr-prediction)
    * [DSSM: Learning Deep Structured Semantic Models for Web Search using Clickthrough Data CIKM2013](advanced-knowledge/deep-learning/learning-deep-structured-semantic-models-for-web-search-using-clickthrough-data)
  * [图卷积网络](advanced-knowledge/graph-convolutional-network)
    * [Graph Convolutional Neural Networks for Web-Scale Recommender Systems KDD2018](advanced-knowledge/graph-convolutional-network/graph-convolutional-neural-networks-for-web-scale-recommender-systems)
  * [强化学习](advanced-knowledge/reinforcement-learning)
    * [DRN基于深度强化学习的新闻推荐模型](advanced-knowledge/reinforcement-learning/drn-a-deep-reinforcement-learning-framework-for-news-recommendation)
* [业界应用](industry-application)
  * [YouTube](https://github.com/luweikxy/machine-learning-notes/tree/123475954b5254a8109117f87d962d5037609eec/content/recommender-systems/industry-application/youtube/youtube.md)
    * [Deep Neural Networks for YouTube Recommendations RecSys2016](https://github.com/luweikxy/machine-learning-notes/tree/123475954b5254a8109117f87d962d5037609eec/content/recommender-systems/industry-application/youtube/deep-neural-networks/Deep-Neural-Networks-for-YouTube-Recommendations.md)
  * [Alibaba](industry-application/alibaba)
    * [Learning Tree-based Deep Model for Recommender Systems KDD2018](industry-application/alibaba/learning-tree-based-deep-model-for-recommender-systems)
    * [Deep Interest Network for Click-Through Rate Prediction KDD2018](industry-application/alibaba/deep-interest-network-for-click-through-rate-prediction)
    * [DSIN:Deep Session Interest Network for Click-Through Rate Prediction IJCAI2019](industry-application/alibaba/dsin-deep-session-interest-network-for-click-through-rate-prediction)
