- UC Berkeley's CS 186 Course Notes: https://cs186berkeley.net/notes/
- Online Course Notes on UW-Madison's Operating Systems: Three Easy Pieces (Jose Hu): https://www.josehu.com/assets/file/ostep-note/operating-systems-ostep.html
- UC Berkeley's CS 61B Online Course Textbook: https://cs61b-2.gitbook.io/cs61b-textbook
- UC Berkeley's Data 100 Online Course Textbook: https://ds100.org/course-notes/
- UC Berkeley's Data 100 Fall 2025 Final Reference Sheet: https://ds100.org/fa26/assets/exams/fa25/fa25_final_reference_sheet.pdf
- UC Berkeley's CS 161 Online Course Textbook: https://textbook.cs161.org/
- UC Berkeley's CS 168 Online Course Textbook: https://textbook.cs168.io/
- UC Berkeley's CS 61C Online Course Notes: https://notes.cs61c.org/
- RV32I Green Card: https://notes.cs61c.org/content/misc/rv32i-green-card
- MIT's 6.390 Online Course Notes: https://introml.mit.edu/notes/
- Illustrated Transformer (Jay Alammar): https://jalammar.github.io/illustrated-transformer/
- PyTorch Learn the Basics: https://docs.pytorch.org/tutorials/beginner/basics/intro.html
- PyTorch Cheat Sheet: https://docs.pytorch.org/tutorials/beginner/ptcheat.html
- Stanford CS 229 Machine Learning Cheatsheets (Afshine & Shervine Amidi): https://stanford.edu/~shervine/teaching/cs-229/
- Reinforcement Learning from Human Feedback and LLM Post-Training (Nathan Lambert): https://rlhfbook.com/
- RL Cheatsheet (Nathan Lambert): https://rlhfbook.com/rl-cheatsheet/
- DeepSeek's GRPO (Group Relative Policy Optimization) (Julia Turc): https://www.youtube.com/watch?v=xT4jxQUl0X8
- Large Transformer Model Inference Optimization (Lilian Weng): https://lilianweng.github.io/posts/2023-01-10-inference-optimization/
- Zero to Mastery Learn PyTorch for Deep Learning (Daniel Bourke): https://www.learnpytorch.io/
- Deep Learning with PyTorch: A 60 Minute Blitz: https://docs.pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html
- Little Book of Linear Algebra: https://little-book-of.github.io/linear-algebra/
- 3Blue1Brown's Essence of Linear Algebra: https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
- Introduction to Linear Algebra for Applied Machine Learning (Pablo Caceres): https://pabloinsente.github.io/intro-linear-algebra
- Linear Algebra Explained in Four Pages (Ivan Savov): https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf
- NVIDIA CUDA Programming Guide: https://docs.nvidia.com/cuda/cuda-programming-guide/index.html
- NVIDIA Modern CUDA Toolbox: https://developer.nvidia.com/blog/the-modern-cuda-toolbox-in-practice-a-step-by-step-optimization-walkthrough/
- GPU Glossary (Modal): https://modal.com/gpu-glossary
- Learn Kernels: https://learn-kernels.com/
- LearnCpp Modern C++ Tutorial: https://www.learncpp.com/
- Learn C++ Interactive Tutorial: https://www.learn-cpp.org/
- Hacking C++ Cheat Sheets and Infographics: https://hackingcpp.com/
- Computer Graphics from Scratch (Gabriel Gambetta): https://gabrielgambetta.com/computer-graphics-from-scratch/
- Introduction to Robotics and Perception (Frank Dellaert & Seth Hutchinson): https://www.roboticsbook.org/
- Tech Interview Handbook Algorithms Study Cheatsheets: https://www.techinterviewhandbook.org/algorithms/study-cheatsheet/
- Leetcode Patterns (Sean Prashad): https://seanprashad.com/leetcode-patterns/
- NeetCode 150 and Blind 75 Anki Deck: https://github.com/envico801/Neetcode-150-and-Blind-75
- Python for Coding Interviews (NeetCode): https://www.youtube.com/watch?v=0K_eZGS5NsU
- NeetCode Roadmap: https://neetcode.io/roadmap
- APUSH Period Reviews in 10 Minutes (Adam Norris): https://www.youtube.com/watch?v=_p_dNOpqdj0&list=PLlair5BOIPJaUm7qr07c7J-A_zyt2dH3I
- Microeconomics - Everything You Need to Know (Jacob Clifford): https://www.youtube.com/watch?v=1UxA6JzoT-4
- Macroeconomics - Everything You Need to Know (Jacob Clifford): https://www.youtube.com/watch?v=MKO1icFVtDc
- Cornell's CS 4120 Lecture Notes: https://www.cs.cornell.edu/courses/cs4120/2026sp/notes/