This repository lists links to high-quality books and open Computer Science courses taught by world-class universities and talented individuals.
Pull requests and issues are welcome!
- UC Berkeley – CS162: Operating Systems and System Programming (Fall 2020)
- MIT – 6.S081: Operating Systems (Fall 2020)
- University of Virginia – CS4414: Operating Systems in Rust
- University of Massachusetts – CS377: Operating Systems
- Bilkent University – CS342: Operating Systems
- Philipp Oppermann – Writing an OS in Rust (Blog)
- CS Primer – Operating Systems
- You Are the OS (Game)
- University of Wisconsin–Madison – Operating Systems: Three Easy Pieces (Book)
- Brown University – CS173: Programming Languages
- San Jose State University – CMPE152: Compiler Design (Winter 2021)
- Cornell University – CS6120: Advanced Compilers (Self-Guided)
- California State University – CSC151: Compiler Construction
- MIT – 6.035: Computer Language Engineering
- Arizona State University – CSE340: Principles of Programming Languages
- Immo Landwerth – Building a Compiler in C# (by Immo Landwerth, C# design team member at Microsoft)
- Catholic University of Louvain – LINGI2132: Languages and Translators
- Static Program Analyses
- Compiler Design (Playlist)
- LLVM + MLIR: How to Build a Compiler
- Stanford – Compilers
- Stanford Online – Compilers (edX)
- MIT – Structure and Interpretation of Computer Programs (Book, SICP)
- Writing an Interpreter in Go (Book)
- Writing a Compiler in Go (Book)
- Douglas Thain, University of Notre Dame - Intro to Compilers & Language Design (Book)
- Robert Nystrom – Crafting Interpreters (Book)
- Nora Sandler – Writing a C Compiler (Book)
- C++ Links – Compilers Resources
- NetworkChuck – Free CCNA 200-301 + You Suck at Subnetting
- Jim Kurose – Computer Networking: A Top-Down Approach (8th Ed.)
- Ben Eater – Networking Tutorials
- Game Networking Resources
- Eli the Computer Guy – Networking
- Beej’s Guide to Network Programming
- Stanford – CS231n: Convolutional Neural Networks
- Stanford – CS231n (with Andrej Karpathy)
- Andrej Karpathy – Neural Networks: Zero to Hero
- Stanford – CS229: Machine Learning (with Andrew Ng)
- Carnegie Mellon University – Deep Learning
- UC Berkeley – CS188: Introduction to AI
- Cornell – CS4780: Machine Learning for Decision Making
- MIT – 6.S191: Introduction to Deep Learning (Updated Yearly)
- MIT – 6.S094: Machine Learning (with Lex Fridman)
- The Coding Train – Neural Networks
- Weights & Biases – Math for Machine Learning
- Duke University – Data Science Math Skills (Coursera)
- DeepLearning.AI + Stanford – ML Specialization (Coursera)
- Data Science in Python (DataQuest)
- ETH Zürich – Mathematics of Machine Learning (Spring 2021)
- Imperial College London – Mathematics for ML: Linear Algebra
- Imperial College London – Mathematics for ML: Multivariate Calculus
- Ian Goodfellow, Yoshua Bengio, Aaron Courville – Deep Learning (Book)
- Stanford – Mathematics for Machine Learning (Book)
- Ronald Kneusel – Math for Deep Learning (Book)
- Carnegie Mellon University – Intro to Database Systems (Fall 2024)
- Carnegie Mellon University – Advanced Database Systems (Spring 2024)
- Carnegie Mellon University – 15-799: Query optimization (Spring 2025)
- UC Berkeley – CS186: Introduction to Database Systems
- MIT – 6.824: Distributed Systems
- Martin Kleppmann – Distributed Systems Lecture Series (Designing Data-Intensive Applications)
- UC Berkeley – CS61C: Great Ideas in Computer Architecture
- Carnegie Mellon University – 15-213: Introduction to Computer Systems
- Carnegie Mellon University – Computer Architecture (Playlist)
- Ron White – How Computers Really Work (Book)
- Bryant & O’Hallaron – Computer Systems: A Programmer’s Perspective (CS:APP Book)
- Neal Ford – Fundamentals of Software Architecture (Book)
- Neal Ford – Software Architecture: The Hard Parts (Book)
- Sam Newman – Building Microservices (Book)
- Martin Kleppmann – Designing Data-Intensive Applications (Book)
- MIT – 18.06SC: Linear Algebra
- MIT – 18.06 Linear Algebra with Gilbert Strang
- 3Blue1Brown – Essence of Linear Algebra
- DeepLearning.AI – Machine Learning: Linear Algebra (Coursera)
- MIT – Mathematics for Computer Science
- TODO
- TODO
- Harvard – Statistics 110: Probability (Joe Blitzstein)
- Will Kurt – Statistics the Fun Way (Book)
- Alex Reinhart – Statistics Done Wrong (Book)
- MIT – Introduction to Algorithms
- UC Berkeley – CS61B: Data Structures
- Harvard – COMPSCI 224: Advanced Algorithms
- Algorithms: Jeff Erickson (Free Book)
- MIT – 6.858: Computer Systems Security (Spring 2020)
- Stanford – CS253: Web Security
- Arizona State University – CSE545: Software Security
- pwn.college – Free Security Training
- Sam Grubb – How Cybersecurity Really Works (Book)
- Malcolm McDonald – Web Security for Developers (Book)
- GOsling – A Tour of Go Resources
- Golang Internals Resources
- Dmitry Vyukov – Go Internals (Book, Partial)
- StackOverflow – How to Learn Go Internals
- Matt Holiday – Go Class (YouTube)