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Science and art, life in between.科学与艺术,生活在其间。

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Changkun Ou

Changkun Ou

Human-AI interaction researcher, engineer, and writer.人机交互研究者、工程师、写作者。

Bridging HCI, AI, and systems programming. Building intelligent human-in-the-loop optimization systems. Informed by psychology, sociology, cognitive science, and philosophy.连接人机交互、AI 与系统编程。构建智能的人在环优化系统。融合心理学、社会学、认知科学与哲学。

Science and art, life in between.科学与艺术,生活在其间。

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Changkun's Blog欧长坤的博客

Specialization专题

Specialization

Last updated: 2018/05/23

This page collects article series written by the author.


Statistical Learning Theory

  1. UMSLT01: A Brief History of Regularization Chinese
  2. UMSLT02: A Brief History of Neural Networks Chinese
  3. UMSLT03: A Gentle Start of Learning Theory Chinese
  4. UMSLT04: The Past and Present of SGD Chinese
  5. UMSLT05: The Consistency of the Learning Process
  6. UMSLT06: Theorem of Large Numbers in Functional Spaces
  7. UMSLT07: The Entropy of a Functional Set
  8. UMSLT08: The Theory of Non-falsifiability
  9. UMSLT09: Three Milestones in Learning Theory
  10. UMSLT10: Foundations of Probability and Statistics Theory
  11. UMSLT11: Glivenko-Cantelli Theorem and More
  12. UMSLT12: Basic Bounds on the Rate of Convergence
  13. UMSLT13: Growth Function and VC Dimension
  14. UMSLT14: Structural Risk Minimization and Minimum Description Length Principle
  15. UMSLT15: Methods of Solving Ill-Posed Problems
  16. TBA

Machine Learning Tutorial

GitHub: https://github.com/changkun/ss18-machine-learning-tutorial


Human Computation

  1. Crowdsourcing and the Wisdom of Crowds (Chinese)
  2. Games with a Purpose (GWAPs) (Chinese)
  3. Input Data Aggregation and Link Prediction (Chinese)
  4. Input Data Aggregation and PageRank (Chinese)
  5. Credit Risk Rating Models and Eigenvector Centrality (Chinese)
  6. Prediction Markets (Chinese)
  7. Social Behavior Analysis (Chinese)
  8. Pitfalls in Data Science (Chinese)
  9. Solutions to Pitfalls (Chinese)
  10. Incentives for System Participation (Chinese)

Mathematical Modeling

Mathematical Modeling Problems (Self-made)

  • Link (Chinese)

Introduction to Mathematical Modeling Tutorial

  • YouTube Playlist
  • Table of Contents
    • Lecture 1: What Is Mathematics
    • Lecture 2: Calculus Review
    • Lecture 3: Understanding Matrices
    • Lecture 3: The Miraculous Fixed Point
    • Lecture 3: The Fastest Way to Scroll
    • Lecture 4: Probability Teaches You to Lie
    • Lecture 4: Birthday Problem, Corruption, and Computation Optimization
    • Lecture 4: The Gambler’s Ruin Theorem
    • Lecture 5: Simple Signal Error Correction
    • Lecture 6: Queueing Theory and Birth-Death Processes
    • Lecture 7: Queue Systems and Monte Carlo Methods Part 1 — Linked Queue and Service Counter
    • Lecture 7: Queue Systems and Monte Carlo Methods Part 2 — Timeline and Simulator (Part I)
    • Lecture 7: Queue Systems and Monte Carlo Methods Part 3 — Simulator (Part II) and Bug Fixes
    • Lecture 7: Probabilistic Programming and CPU Resource Contention
    • Lecture 8: Markov Prediction — Transition Probability and Information Dissipation
    • Final Lecture: Hidden Markov Models — Observable vs. Unobservable, Three Fundamental Problems
    • Final Lecture: Markov Models — Three Fundamental Problems and Stock Market Prediction (Part I)
    • Final Lecture: Markov Models — Stock Market Prediction (Part II)

The Fastest Guide towards Modern C++ (C++11/14/17)

  • GitHub: https://github.com/changkun/modern-cpp-tutorial/

C++ Project Tutorial Series

Note: This series was written by the author for Shiyanlou as a hands-on C++ tutorial series.

Phase 1: Basics

  • C++ Solar System Simulation Chinese
  • C++ Dynamic Object Tracking Chinese
  • C++ Bank Queue Service Simulation Chinese
  • C++ First-Person Shooter Game Chinese

Phase 2: Intermediate

  • Thread Pool in 100 Lines of C++ Chinese
  • High-Performance Memory Pool in C++ Chinese
  • C++ Web Service Framework (HTTP+HTTPS) Chinese
  • C++ Markdown Parser Chinese

Phase 3: Advanced

  • C++ Docker Container Implementation Chinese
  • C++ Memory Leak Detector Chinese
  • High-Performance RTTI Library in C++ Chinese

Electron in Practice

  • Electron Deep Dive: Practical Summary (Chinese)

Lua in One Day

  1. Mac Environment Setup and Basic Syntax (Chinese)
  2. Table and Array (Chinese)
  3. OOP — Table Copying (Chinese)
  4. OOP — Function Closures (Chinese)
  5. Cocos2d-x and Lua (Chinese)

Linux Beginner Notes

  1. Linux Basics and File System Structure (Chinese)
  2. Essential Linux Commands (Chinese)
  3. Essential Vim Commands (Chinese)
  4. Linux File System Operations (Chinese)
  5. UGO Model and Linux Permissions (Chinese)
  6. Linux Networking and Configuration (Chinese)
  7. Getting Help (Chinese)

Linux Kernel Analysis

  1. How Computers Work (Chinese)
  2. Time-Slice Round-Robin Scheduling (Chinese)
  3. Analysis of start_kernel() (Chinese)
  4. System Calls (Chinese)
  5. system_call Interrupt Process (Chinese)
  6. Process Creation (Chinese)

ELK in Practice

  1. ELK+Redis Best Practices (Chinese)
  2. ELK on Docker Best Practices

Docker Quick Start Tutorial

  1. Docker Basic Concepts and Operations (Chinese)
  2. Docker Image and Container Management (Chinese)
  3. Docker Data and Network Management & Docker Security
  4. Docker and Dockerfile
  5. Docker Operations I: Docker Compose
  6. Docker Operations II: Docker Swarm and Kubernetes
  7. Docker API and Development

专题

最后更新:2018/05/23

本页面收录了本人编写的系列文章。


统计学习理论 (Statistical Learning Theory)

  1. UMSLT01: A Brief History of Regularization 中文
  2. UMSLT02: A Brief History of Neural Networks 中文
  3. UMSLT03: A Gentle Start of Learning Theory 中文
  4. UMSLT04: The Past and Present of SGD 中文
  5. UMSLT05: The Consistency of the Learning Process
  6. UMSLT06: Theorem of Large Numbers in Functional Spaces
  7. UMSLT07: The Entropy of a Functional Set
  8. UMSLT08: The Theory of Non-falsifiability
  9. UMSLT09: Three Milestones in Learning Theory
  10. UMSLT10: Foundations of Probability and Statistics Theory
  11. UMSLT11: Glivenko-Cantelli Theorem and More
  12. UMSLT12: Basic Bounds on the Rate of Convergence
  13. UMSLT13: Growth Function and VC Dimension
  14. UMSLT14: Structural Risk Minimization and Minimum Description Length Principle
  15. UMSLT15: Methods of Solving Ill-Posed Problems
  16. TBA

Machine Learning Tutorial

GitHub: https://github.com/changkun/ss18-machine-learning-tutorial


人肉计算 (Human Computation)

  1. 众包与群众智慧
  2. 意图博弈 GWAPs
  3. 输入数据聚合与链路预测
  4. 输入数据聚合与PageRank
  5. 信用风险评级模型与特征向量中心性
  6. 预测市场
  7. 社会行为分析
  8. 数据科学中的陷阱
  9. 陷阱的解法
  10. 系统参与的激励

数学建模 (Mathematical Modeling)

数学建模试题(自制)

  • 链接

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++)

  • GitHub: https://github.com/changkun/modern-cpp-tutorial/

C++ 系列项目教程

说明: 本系列是博主专为实验楼编写的C++实战系列教程。

第一阶段: 基础

  • C++ 实现太阳系行星系统 中文
  • C++ 实现动态目标的追踪 中文
  • C++ 实现银行排队服务模拟 中文
  • C++ 实现第一人称射击游戏 中文

第二阶段: 进阶

  • 100 行 C++ 代码实现线程池 中文
  • C++ 实现高性能内存池 中文
  • C++ 开发 Web 服务框架(HTTP+HTTPS) 中文
  • C++ 打造 Markdown 解析器 中文

第三阶段: 高级

  • C++ 实现 Docker 容器 中文
  • C++ 实现内存泄露检查器 中文
  • C++ 实现高性能 RTTI 库 中文

Electron 实践系列

  • Electron 深度实践总结

Lua 一日游

  1. Mac环境搭建与基本语法
  2. Table 与 Array
  3. 面向对象——复制表形式
  4. 面向对象——函数闭包
  5. cocos2dx 与 Lua

Linux 入门笔记

  1. Linux基本操作及文件系统结构
  2. Linux常用必备命令
  3. Vim必备命令
  4. Linux文件系统基本操作
  5. UGO模型与Linux权限
  6. Linux网络及相关配置
  7. 获取帮助

Linux 内核分析

  1. 计算机工作原理
  2. 时间片轮转调度
  3. start_kernel()过程分析
  4. 系统调用
  5. system_call中断过程
  6. 进程的创建过程

ELK 若干实践

  1. ELK+Redis 最佳实践
  2. ELK on Docker 最佳实践

Docker 极速入门教程

  1. Docker 基本概念和操作
  2. Docker 镜像与容器管理
  3. Docker 数据与网络管理及 Docker 安全
  4. Docker 与 Dockerfile
  5. Docker 运维之一: Docker Compose
  6. Docker 运维之二: Docker Swarm 及 Kubernetes
  7. Docker API 及其开发

Have thoughts on this?有想法?

I'd love to hear from you — questions, corrections, disagreements, or anything else.欢迎来信交流——问题、勘误、不同看法,或任何想说的。

hi@changkun.de
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