python学习之机器学习

灰太狼 2022-04-22 02:00 483阅读 0赞

## R语言学习笔记 ##

### R实现机器学习 ###

[R机器学习（一）——一元回归][R]

[R机器学习（二）——非线性回归][R 1]

[R机器学习（三）——多元回归][R 2]

[R机器学习（四）——logistics回归][R_logistics]

[R机器学习（五）——决策树ID3算法][R_ID3]

R机器学习（六）——决策树C4.5算法

R机器学习（七）——回归树CART算法

R机器学习（八）——K-means聚类

R机器学习（九）——层次聚类

R机器学习（十）——密度聚类DBSCAN

R机器学习（十一）——Apriori关联规则

R机器学习（十二）——SVM

R机器学习（十三）——时间序列模型

R机器学习（十四）——主成分分析

R机器学习（十五）——朴素贝叶斯

R机器学习（十六）——KNN算法

R机器学习（十七）——梯度提升决策树（GBDT）

R机器学习（十八）——随机森林

R机器学习（十九）——Adaboost算法

[R机器学习（二十）——BP神经网络][R_BP]

[R机器学习（二十一）——基于Tensorflow实现神经网络][R_Tensorflow]

## Python学习笔记 ##

### Python基础 ###

[常用文件读取][Link 1]

[Python2与Python3的不同][Python2_Python3]

[Python正则表达式][Python]

[基于python的urllib库编写网络爬虫  
][python_urllib_]

[人脸检测工具face\_recognition的安装与使用][face_recognition]

[Mac安装图像标注工具LabelImg][Mac_LabelImg]

[Mac Python安装liblinear机器学习库][Mac Python_liblinear]

[用pip安装python库下载超时的解决办法][pip_python]

### Python数据结构 ###

[Python数据结构（一）——线性表：顺序表的实现][Python 1]

Python数据结构（二）——线性表：链表的实现

[Python数据结构（三）——字符串：KMP算法实现][Python_KMP]

[Python数据结构（四）——排序：冒泡排序][Python 2]

[Python数据结构（五）——排序：直接选择排序][Python 3]

[Python数据结构（六）——排序：插入排序][Python 4]

[Python数据结构（七）——排序：快速排序][Python 5]

Python数据结构（）——排序：堆排序

Python数据结构（）——排序：归并排序

### Python模块 ###

[Python常用模块（一）——shutil模块][Python_shutil]

[Python常用模块（二）——os模块][Python_os]

[Python常用模块（三）——collections模块的常用方法][Python_collections]

### Matplotlib画图 ###

[Matplotlib画图（一）——散点图scatter  
][Matplotlib_scatter] [Matplotlib画图（二）——根据函数公式画图  
][Matplotlib_]

[Matplotlib画图（三）——分段函数  
][Matplotlib_ 1]

[Matplotlib画图（四）——折线图  
][Matplotlib_ 2]

[Matplotlib画图（五）——多图片共框  
][Matplotlib_ 3]

[Matplotlib画图（六）——图中图  
][Matplotlib_ 4]

[Matplotlib画图（七）——线的颜色、点的形状  
][Matplotlib_ 5]

[Matplotlib画图（八）——条形图  
][Matplotlib_ 6]

[Matplotlib画图（九）——饼图  
][Matplotlib_ 7]

[Matplotlib画图（十）——基于networkx画关系网络图  
][Matplotlib_networkx_]

[Matplotlib画图（十一）——箱线图][Matplotlib]

### scikit-learn实现机器学习 ###

[【scikit-learn】机器学习（一）——一元回归模型][scikit-learn]

【scikit-learn】机器学习（二）——非线性回归

【scikit-learn】机器学习（三）——多元回归

【scikit-learn】机器学习（四）——logistics回归

【scikit-learn】机器学习（五）——决策树ID3算法

【scikit-learn】机器学习（六）——决策树C4.5算法

【scikit-learn】机器学习（七）——回归树CART算法

[【scikit-learn】机器学习（八）——K-means聚类  
][scikit-learn_K-means_]

【scikit-learn】机器学习（九）——层次聚类

【scikit-learn】机器学习（十）——密度聚类DBSCAN

【scikit-learn】机器学习（十一）——Apriori关联规则

【scikit-learn】机器学习（十二）——SVM

【scikit-learn】机器学习（十三）——时间序列模型

【scikit-learn】机器学习（十四）——主成分分析

【scikit-learn】机器学习（十五）——朴素贝叶斯

【scikit-learn】机器学习（十六）——KNN算法

【scikit-learn】机器学习（十七）——梯度提升决策树（GBDT）

【scikit-learn】机器学习（十八）——随机森林

【scikit-learn】机器学习（十九）——Adaboost算法

【scikit-learn】机器学习（二十）——BP神经网络

【scikit-learn】机器学习（二十一）——基于Tensorflow实现神经网络

[【scikit-learn】机器学习（二十二）——图片聚类：k-means算法的python实现][scikit-learn_k-means_python]

[  
][Link 2]

## Machine Learning学习笔记 ##

[【Trick】决策树剪枝][Trick]

[【Trick】核技巧][Trick 1]

[【Trick】数据预处理的常用方法][Trick 2]

[【Trick】机器学习特征工程处理（一）][Trick 3]

[【异同】信息增益、Gini、信息增益率][Gini]

[【异同】决策树ID3和C4.5的异同][ID3_C4.5]

[【异同】LR和SVM的异同][LR_SVM]

[【异同】梯度提升决策树和随机森林的异同][Link 3]

[【ML算法】监督学习——线性回归][ML]

[【ML算法】监督学习——逻辑回归][ML 1]

[【ML算法】监督学习——决策树ID3算法][ML_ID3]

【ML算法】监督学习——决策树C4.5算法

[【ML算法】监督学习——支持向量机][ML 2]  
[【ML算法】监督学习——KNN算法][ML_KNN]  
[【ML算法】无监督学习——K-means聚类][ML_K-means]

【ML算法】无监督学习——层次聚类

【ML算法】无监督学习——DBCAN聚类

[【ML算法】无监督学习——关联规则Apriori][ML_Apriori]

【ML算法】集成学习——随机森林（RF）

【ML算法】集成学习——GBDT

【ML算法】集成学习——Adaboost

【ML算法】集成学习——xgboost

## Machine Learning算法底层实现（基于python） ##

【ML算法实现】监督学习——线性回归

【ML算法实现】监督学习——逻辑回归

【ML算法实现】监督学习——决策树ID3算法

【ML算法实现】监督学习——决策树C4.5算法

【ML算法实现】监督学习——支持向量机  
【ML算法实现】监督学习——KNN算法  
【ML算法实现】无监督学习——K-means聚类

【ML算法实现】无监督学习——层次聚类

【ML算法实现】无监督学习——DBCAN聚类

【ML算法实现】无监督学习——关联规则Apriori

【ML算法实现】集成学习——随机森林（RF）

【ML算法实现】集成学习——GBDT

【ML算法实现】集成学习——Adaboost

【ML算法实现】集成学习——xgboost

## 推荐系统专题 ##

[推荐系统——算法概述][Link 4]

推荐系统——冷启动问题

## Deep Learning学习笔记 ##

### 算法学习 ###

[Deep Learning常用数据集][Deep Learning]

[相对熵与交叉熵][Link 5]

[AutoEncoder详解][AutoEncoder]

[VAE详解][VAE]

[相似图片搜索算法介绍][Link 6]

[常见超分辨率处理方法][Link 7]

[防止过拟合的处理方法][Link 8]

### Tensorflow ###

[卷积神经网络简介][Link 9]

[基于Tensorflow实现AutoEncoder][Tensorflow_AutoEncoder]

[LeNet5神经网络简介及TensorFlow实现  
][LeNet5_TensorFlow_]

[VGG网络详解及代码实现][Tensorflow_AutoEncoder]

[Tensorflow实现CNN][Tensorflow_CNN]

### Keras ###

[python机器学习算法（Keras）  
][python_Keras_]

[基于Keras实现CNN][Keras_CNN]

[用RNN做MNIST分类][RNN_MNIST]

### Pytorch ###

[Pytorch实现CNN卷积神经网络][Pytorch_CNN]

### Caffe ###

[Mac安装caffe时出现的错误][Mac_caffe]

## DL&ML项目实践 ##

[【ML项目】基于决策树算法的电信运营商客户流失预测][ML 3]

[【ML项目】基于聚类算法的我国临终关怀现状分析][ML 4]  
【ML项目】基于情感分析和关系网络的影视产品文本挖掘分析  
[【ML项目】基于网络爬虫和数据挖掘算法的web招聘数据分析（一）——数据获取及预处理][ML_web]  
【ML项目】基于网络爬虫和数据挖掘算法的web招聘数据分析（二）——探索性数据分析  
【ML项目】基于网络爬虫和数据挖掘算法的web招聘数据分析（三）——数据挖掘算法建模  
[【DL项目】跑CNN程序时遇到的error(持续更新)][DL_CNN_error]

[【DL项目】你的颜值打几分？基于Tensorflow实现人脸打分][DL_Tensorflow]

## Reinforvement Learning学习笔记 ##

[基于tensorflow实现RL的policy based算法][tensorflow_RL_policy based]

## Hadoop&Spark学习 ##

[【Hadoop&Spark】安装详解][Hadoop_Spark]

## Docker学习 ##

[运行Docker时遇到的一些问题][Docker]

## 论文笔记 ##

### 论文翻译 ###

[Support Vector Clustering 论文翻译（未完待续）  
][Support Vector Clustering _]

[Generative Adversarial Nets论文翻译(未完待续)][Generative Adversarial Nets]  
[Convolutional Clustering for Unsupervised Learning 论文翻译][Convolutional Clustering for Unsupervised Learning]

[Very Deep Convolutional Network For Large-Scale Image Recognition 论文翻译（VGG）  
][Very Deep Convolutional Network For Large-Scale Image Recognition _VGG_][Dynamic Routing Between Capsules论文翻译（中英文对照）（更新中）][Dynamic Routing Between Capsules]

### 论文理解 ###

[Generative Adversarial Nets 论文理解][Generative Adversarial Nets 1]  
[Convolutional Clustering for Unsupervised Learning 论文理解][Convolutional Clustering for Unsupervised Learning 1]

[Very Deep Convolutional Networks for Large-Scale Image Recognition——VGG（论文理解）][Very Deep Convolutional Networks for Large-Scale Image Recognition_VGG]

## 编程问题 ##

[网易有道笔试题——数字位数全排列整除问题  
][Link 10]

[金山云笔试题——字母密码转换排列问题  
][Link 11]

[R]: http://blog.csdn.net/roguesir/article/details/78067993
[R 1]: http://blog.csdn.net/roguesir/article/details/78068005
[R 2]: http://blog.csdn.net/roguesir/article/details/77847191
[R_logistics]: http://blog.csdn.net/roguesir/article/details/78088650
[R_ID3]: http://blog.csdn.net/roguesir/article/details/78147616
[R_BP]: http://blog.csdn.net/roguesir/article/details/76573096
[R_Tensorflow]: http://blog.csdn.net/roguesir/article/details/75810176
[Link 1]: http://blog.csdn.net/roguesir/article/details/77748180
[Python2_Python3]: http://blog.csdn.net/roguesir/article/details/76736474
[Python]: http://blog.csdn.net/roguesir/article/details/74940113
[python_urllib_]: http://blog.csdn.net/roguesir/article/details/74923232
[face_recognition]: http://blog.csdn.net/roguesir/article/details/77104246
[Mac_LabelImg]: http://blog.csdn.net/roguesir/article/details/73647695
[Mac Python_liblinear]: https://blog.csdn.net/roguesir/article/details/79793569
[pip_python]: http://blog.csdn.net/roguesir/article/details/76813173
[Python 1]: http://blog.csdn.net/roguesir/article/details/78257555
[Python_KMP]: http://blog.csdn.net/roguesir/article/details/78319845
[Python 2]: http://blog.csdn.net/roguesir/article/details/78352795
[Python 3]: http://blog.csdn.net/roguesir/article/details/78353794
[Python 4]: http://blog.csdn.net/roguesir/article/details/78354360
[Python 5]: http://blog.csdn.net/roguesir/article/details/78355563
[Python_shutil]: http://blog.csdn.net/roguesir/article/details/78181523
[Python_os]: http://blog.csdn.net/roguesir/article/details/78181570
[Python_collections]: https://blog.csdn.net/roguesir/article/details/79657608
[Matplotlib_scatter]: http://blog.csdn.net/roguesir/article/details/75037571
[Matplotlib_]: http://blog.csdn.net/roguesir/article/details/77839283
[Matplotlib_ 1]: http://blog.csdn.net/roguesir/article/details/77839438
[Matplotlib_ 2]: http://blog.csdn.net/roguesir/article/details/77839721
[Matplotlib_ 3]: http://blog.csdn.net/roguesir/article/details/77839818
[Matplotlib_ 4]: http://blog.csdn.net/roguesir/article/details/77854607
[Matplotlib_ 5]: http://blog.csdn.net/roguesir/article/details/77932526
[Matplotlib_ 6]: http://blog.csdn.net/roguesir/article/details/78178365
[Matplotlib_ 7]: http://blog.csdn.net/roguesir/article/details/78178342
[Matplotlib_networkx_]: http://blog.csdn.net/roguesir/article/details/78211580
[Matplotlib]: http://blog.csdn.net/roguesir/article/details/78249864
[scikit-learn]: http://blog.csdn.net/roguesir/article/details/77884128
[scikit-learn_K-means_]: http://blog.csdn.net/roguesir/article/details/78273586
[scikit-learn_k-means_python]: http://blog.csdn.net/roguesir/article/details/74283268
[Link 2]: http://blog.csdn.net/roguesir/article/details/77886996
[Trick]: http://blog.csdn.net/roguesir/article/details/76690931
[Trick 1]: http://blog.csdn.net/roguesir/article/details/78803959
[Trick 2]: http://blog.csdn.net/roguesir/article/details/78639809
[Trick 3]: http://blog.csdn.net/roguesir/article/details/79183431
[Gini]: http://blog.csdn.net/roguesir/article/details/76619919
[ID3_C4.5]: http://blog.csdn.net/roguesir/article/details/76474709
[LR_SVM]: http://blog.csdn.net/roguesir/article/details/76174879
[Link 3]: http://blog.csdn.net/roguesir/article/details/76173879
[ML]: http://blog.csdn.net/roguesir/article/details/78637391
[ML 1]: http://blog.csdn.net/roguesir/article/details/79616328
[ML_ID3]: http://blog.csdn.net/roguesir/article/details/76628024
[ML 2]: http://blog.csdn.net/roguesir/article/details/78659645
[ML_KNN]: http://blog.csdn.net/roguesir/article/details/78580412
[ML_K-means]: http://blog.csdn.net/roguesir/article/details/78632606
[ML_Apriori]: http://blog.csdn.net/roguesir/article/details/78742843
[Link 4]: http://blog.csdn.net/roguesir/article/details/79010513
[Deep Learning]: http://blog.csdn.net/roguesir/article/details/73790619
[Link 5]: http://blog.csdn.net/roguesir/article/details/77481968
[AutoEncoder]: http://blog.csdn.net/roguesir/article/details/77469665
[VAE]: http://blog.csdn.net/roguesir/article/details/77480923
[Link 6]: http://blog.csdn.net/roguesir/article/details/77449682
[Link 7]: http://blog.csdn.net/roguesir/article/details/75214030
[Link 8]: http://blog.csdn.net/roguesir/article/details/78225059
[Link 9]: http://blog.csdn.net/roguesir/article/details/73741216
[Tensorflow_AutoEncoder]: http://blog.csdn.net/roguesir/article/details/77051250
[LeNet5_TensorFlow_]: http://blog.csdn.net/roguesir/article/details/73770448
[Tensorflow_CNN]: http://blog.csdn.net/roguesir/article/details/75030354
[python_Keras_]: http://blog.csdn.net/roguesir/article/details/74940390
[Keras_CNN]: http://blog.csdn.net/roguesir/article/details/74547787
[RNN_MNIST]: http://blog.csdn.net/roguesir/article/details/74536423
[Pytorch_CNN]: http://blog.csdn.net/roguesir/article/details/76577552
[Mac_caffe]: http://blog.csdn.net/roguesir/article/details/75029011
[ML 3]: http://blog.csdn.net/roguesir/article/details/73658419
[ML 4]: http://blog.csdn.net/roguesir/article/details/74940641
[ML_web]: http://blog.csdn.net/roguesir/article/details/78790409
[DL_CNN_error]: http://blog.csdn.net/roguesir/article/details/74940652
[DL_Tensorflow]: http://blog.csdn.net/roguesir/article/details/77164578
[tensorflow_RL_policy based]: http://blog.csdn.net/roguesir/article/details/76762444
[Hadoop_Spark]: http://blog.csdn.net/roguesir/article/details/78335034
[Docker]: http://blog.csdn.net/roguesir/article/details/76412257
[Support Vector Clustering _]: http://blog.csdn.net/roguesir/article/details/75007749
[Generative Adversarial Nets]: http://blog.csdn.net/roguesir/article/details/76886795
[Convolutional Clustering for Unsupervised Learning]: http://blog.csdn.net/roguesir/article/details/72842958
[Very Deep Convolutional Network For Large-Scale Image Recognition _VGG_]: http://blog.csdn.net/roguesir/article/details/77470043
[Dynamic Routing Between Capsules]: https://blog.csdn.net/roguesir/article/details/79681904
[Generative Adversarial Nets 1]: http://blog.csdn.net/roguesir/article/details/76985648
[Convolutional Clustering for Unsupervised Learning 1]: http://blog.csdn.net/roguesir/article/details/73681885
[Very Deep Convolutional Networks for Large-Scale Image Recognition_VGG]: http://blog.csdn.net/roguesir/article/details/77945732
[Link 10]: http://blog.csdn.net/roguesir/article/details/78089357
[Link 11]: http://blog.csdn.net/roguesir/article/details/78089600