CS计算机自学指南 - AI研究社

CS计算机自学指南

这是一本计算机的自学指南。

由于书内涵盖资源众多,根据不同人群的空闲时间和学习目标制定了对应的使用指南。

一份供参考的 CS 学习规划:我根据自己的自学经历制定的全面的、系统化的 CS 自学规划。

必学工具:一些 CSer 效率工具介绍,例如 IDE, 翻墙, StackOverflow, Git, GitHub, Vim, LaTeX, GNU Make, Docker, 工作流 等等。

经典书籍推荐:你是否苦于教材的晦涩难懂不知所云?别从自己身上找原因了,可能只是教材写得太烂。

看过 CSAPP 这本书的同学一定会感叹好书的重要,我将列举推荐各个计算机领域的必看好书与资源链接。

国内外高质量 CS 课程汇总:我将把我上过的以及开源社区贡献的高质量的国内外 CS 课程分门别类进行汇总,介绍其课程内容特点并给出相应的自学建议,大部分课程都会有一个独立的仓库维护相关的资源以及作业实现供大家学习参考。

CS计算机自学指南 - AI研究社

CS自学指南学习内容包括:如何使用这本书,一个仅供参考的CS学习规划,必学工具,翻墙,Vim,Emacs,Git,GitHub,GNU Make,CMake,LaTeX,Docker,Scoop,日常学习工作流,实用工具箱,毕业论文,信息检索,好书推荐,数学基础,数学基础,MIT18.01/18.02: Calculus,MIT18.06: Linear Algebra,MIT6.050J: Information theory and Entropy,数学进阶,UCB CS70: discrete Math and probability theory,UCB CS126: probability theory,MIT 6.042J: Mathematics for Computer Science,MIT18.330: Introduction to numerical analysis,Standford EE364A: Convex Optimization,The Information Theory, Pattern Recognition, and Neural Networks,编程入门,MIT-Missing-Semester,Sysadmin DeCal,Python 语言,UCB CS61A: Structure and Interpretation of Computer Programs,CS50P: CS50's Introduction to Programming with Python,C 语言,Harvard CS50: This is CS50x,Duke University: Introductory C Programming Specialization,C++ 语言,AmirKabir University of Technology AP1400-2: Advanced Programming,Stanford CS106L: Standard C++ Programming,Stanford CS106B/X,Java 语言,MIT 6.092: Introduction To Programming In Java,Rust 语言,Stanford CS110L: Safety in Systems Programming,KAIST CS431: Concurrent Programming,函数式语言,Cornell CS3110: OCaml Programming Correct + Efficient + Beautiful,Haskell MOOC,电子基础,EE16A&B: Designing Information Devices and Systems I&II,UCB EE120 : Signal and Systems,MIT 6.007 Signals and Systems,数据结构与算法,UCB CS61B: Data Structures and Algorithms,Coursera: Algorithms I & II,MIT 6.006: Introduction to Algorithms,MIT 6.046: Design and Analysis of Algorithms,UCB CS170: Efficient Algorithms and Intractable Problems,软件工程,MIT 6.031: Software Construction,UCB CS169: software engineering,CMU 17-803: Empirical Methods,计算机系统基础,CMU 15-213: CSAPP,Stanford CS110: Principles of Computer Systems,体系结构,Coursera: Nand2Tetris,UCB CS61C: Great Ideas in Computer Architecture,ETHz: Digital Design and Computer Architecture,ETHz: Computer Architecture,操作系统,MIT 6.S081: Operating System Engineering,UCB CS162: Operating System,NJU OS: Operating System Design and Implementation,HIT OS: Operating System,并行与分布式系统,CMU 15-418/Stanford CS149: Parallel Computing,MIT 6.824: Distributed System,计算机系统安全,UCB CS161: Computer Security,MIT 6.1600: Foundations of Computer Security,MIT 6.858: Computer System Security,ASU CSE365: Introduction to Cybersecurity,ASU CSE466: Computer Systems Security,SU SEED Labs,计算机网络,USTC Computer Networking:A Top-Down Approach,Computer Networking: A Top-Down Approach,Stanford CS144: Computer Network,数据库系统,UCB CS186: Introduction to Database System,CMU 15-445: Database Systems,Caltech CS122: Database System Implementation,Stanford CS346: Database System Implementation,CMU 15-799: Special Topics in Database Systems,编译原理,PKU 编译原理实践,Stanford CS143: Compilers,NJU 编译原理,KAIST CS420: Compiler Design,编程语言设计与分析,Stanford CS242: Programming Languages,NJU 软件分析,Cambridge: Semantics of Programming Languages,计算机图形学,GAMES101,GAMES202,GAMES103,Stanford CS148,CMU 15-462,USTC CG,Web开发,MIT web development course,Stanford CS142: Web Applications,University of Helsinki: Full Stack open 2022,CS571 Building UI (React & React Native),数据科学,UCB Data100: Principles and Techniques of Data Science,人工智能,Harvard CS50's Introduction to AI with Python,UCB CS188: Introduction to Artificial Intelligence,机器学习,Coursera: Machine Learning,Stanford CS229: Machine Learning,UCB CS189: Introduction to Machine Learning,机器学习系统,CMU 10-414/714: Deep Learning Systems,Machine Learning Compilation,深度学习,UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision,Coursera: Deep Learning,国立台湾大学: 李宏毅机器学习,Stanford CS231n: CNN for Visual Recognition,Stanford CS224n: Natural Language Processing,Stanford CS224w: Machine Learning with Graphs,UCB CS285: Deep Reinforcement Learning,机器学习进阶,进阶路线图,CMU 10-708: Probabilistic Graphical Models,Columbia STAT 8201: Deep Generative Models,U Toronto STA 4273 Winter 2021: Minimizing Expectations,Stanford STATS214 / CS229M: Machine Learning Theory。

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