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)
- Donne Martin - System Design Primer (by Donne Martin, tech lead at Meta)
- 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
- MIT - 6.046J: Design and Analysis of Algorithms (Spring 2015)
- 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)