Machine-Learning-notes

Repo Structure(仓库结构)

├──models(所有封装好的类的文件夹)
│  ├──linear_models.py(线性模型集合)
│  │  ├──Linear_regression
│  │  ├──Perceptron
│  │  ├──LDA
│  │  ├──Logistic_regression
│  │  ├──GDA
│  │  ├──Naive_Bayes_classifier
│  ├──decompose_models.py(分解算法集合)
│  │  ├──PCA
├──EN-TeX_files:English version notes in TeX file format for each chapter
│  ├──fundamentals-of-math_gaussian-distribution_expectation&variance
│  ├──fundamentals-of-math_gaussian-distribution_perspective-of-probability
│  ├──fundamentals-of-math_gaussian-distribution_marginal-probability&conditonal-probability
│  ├──fundamentals-of-math_gaussian-distribution_joint-distribution
│  ├──linear-regression
│  ├──linear_classification_perceptron
│  ├──linear_classification_lda
│  ├──linear_classification_logistic-regression
│  ├──linear_classification_gda
│  ├──linear_classification_Naive_Bayes_classify
│  ├──dimension_reduction_principal_component_analysis
│  ├──dimension_reduction_Principal_coordinate_analysis
│  ├──dimension_reduction_Probability_Principal-componant-analysis
├──CN-TeX_files:各章节的中文版TeX格式的ML笔记
│  ├──数学基础_高斯分布_期望方差篇
│  ├──数学基础_高斯分布_概率视角篇
│  ├──数学基础_高斯分布_边缘概率&条件概率
│  ├──数学基础_高斯分布_联合分布
│  ├──线性回归篇
│  ├──线性分类_感知机篇
│  ├──线性分类_线性判别分析篇
│  ├──线性分类_逻辑回归篇
│  ├──线性分类_高斯判别分析篇
│  ├──线性分类_朴素贝叶斯篇
│  ├──降维_主成分分析
│  ├──降维_主坐标分析
│  ├──降维_概率视角的主成分分析
├──Machine-Learning-Notes.tex: an notes set composed by  chapters
├──Machine-Learning-Notes.pdf: compiled from tex file with the same file name
├──机器学习笔记.tex: 由各章节组成的笔记集合
├──机器学习笔记.pdf: 由同名tex文件编译得到
├──figures: stores figures to be compiled
├──README.md

Reference(参考)

repo

videos in bilibili.com