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专题 (Specialization)
Last updates: 2018.045.23
本页面收录了本人编写的系列文章。
统计学习理论 (Statistical Learning Theory)
- UMSLT01: A Breif History of Regularization 中文
- UMSLT02: A Breif History of Neural Networks 中文
- UMSLT03: A Gentle Start of Learning Theory 中文
- UMSLT04: The Past and Present of SGD 中文
- UMSLT05: The consistency of learning process
- UMSLT06: Theorem of Large Numbers in Functional Spaces
- UMSLT07: The Entropy of Functional Set
- UMSLT08: The Theory of Non-falsifiability
- UMSLT09: Three Milstones in Learning Theory
- UMSLT10: Foundation of Probability and Statistics Theory
- UMSLT11: Glivenko-Cantelli Theorem and more
- UMSLT12: Basic Bounds on the Rate of Convergence
- UMSLT13: Growth Function and VC Dimension
- UMSLT14: Structral Risk Minimization and Minimum Description Length Principle
- UMSLT15: Methods of Solving Ill-Posed Problems
Machine Learning Tutorial
Github: https://github.com/changkun/ss18-machine-learning-tutorial
人肉计算(Human Computation)
数学建模 (Mathematical Modeling)
数学建模试题(自制)
数学建模初步教程
- YouTube 合集
- 目录
- 第一讲 何谓数学
- 第二讲 微积分拾遗
- 第三讲 理解矩阵
- 第三讲 神一般的不动点
- 第三讲 怎样刷屏最快
- 第四讲 概率教你说谎
- 第四讲 从生日问题看贪官腐败、计算优化
- 第四讲 赌徒输光定理
- 第五讲 简单信号纠错
- 第六讲 等候理论、生灭过程
- 第七讲 队列系统、蒙特卡洛方法 Part.1 链式队列与服务台的实现
- 第七讲 队列系统、蒙特卡洛方法 Part.2 时间线与模拟器实现(上)
- 第七讲 队列系统、蒙特卡洛方法 Part.3 模拟器(下)与Bug修复
- 第七讲 概率编程、CPU资源争夺
- 第八讲 马尔可夫预测:转移概率与信息耗散
- 最终讲 隐马尔可夫预测: 可观测与不可观测 三大基本问题
- 最终讲 马尔可夫模型:三大基本问题与股市预测(上)
- 最终讲 马尔可夫模型:股市预测(下)
高速上手 C++11/14/17 (The Fastest Guide towards Modern C++)
C++ 系列项目教程
第一阶段: 基础
第二阶段: 进阶
第三阶段: 高级
Electron 实践系列
Lua 一日游
Linux 入门笔记
Linux 内核分析
ELK 若干实践
- ELK+Redis 最佳实践
- [ELK on Docker 最佳实践]
Docker 极速入门教程
- Docker 基本概念和操作
- Docker 镜像与容器管理
- [Docker 数据与网络管理及 Docker 安全]
- [Docker 与 Dockerfile]
- [Docker 运维之一: Docker Compose]
- [Docker 运维之二: Docker Swarm 及 Kubernetes]
- [Docker API 及其开发]
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