Programming, Math, Science

This is a list of links to different freely available learning resources about computer programming, math, and science.

Table of contents

• AI
  • Machine Learning
  • Computer Games AI
• Algorithms
• Art
• Biology
• Command Line and Tools
  • Curl
  • Linux command line
• Compilers and Interpreters
• Computer Graphics
  • Curves and Surfaces
  • Demoscene
  • Image Processing
  • OpenGL
  • Ray Tracing
  • Shaders
  • Vulkan
  • WebGPU
• Computer Networks and Network Programming
• Cryptography
• Data Science
• Debuggers
• Databases
• Design Patterns
• Distributed Systems
• Electronics
• Emulators and Virtual Machines
• Fluids Simultion
• Game Programming
  • Game Engines
• Geographic Information Systems
• GUI Programming
• Hardware
• Logical Games
• Low Level Stuff
• Math
  • Algebra
  • Calculus
  • Category Theory
  • Geometry
  • Probability and Statistics
  • Theoretical Computer Science
  • Topology
• Multithreading and Concurrency
• Operating Systems
  • Operating Systems Development
• Photography
• Physics
  • Simulations
• Programming Languages
  • Ada
  • Assembly
  • Bash
  • Basic
  • C
  • C++
  • D
  • Datalog
  • Erlang
  • Forth
  • Go
  • Haskell
  • Java
  • JavaScript
  • Lean
  • Lisp dialects
    • Clojure
    • Common Lisp
    • Racket
    • Scheme
  • Nim
  • OCaml
  • Oberon-2
  • Odin
  • Pascal
  • PostScript
  • Prolog
  • Pyret
  • Python
  • Regular expressions
  • Ruby
  • Rust
  • Smalltalk
  • Zig
• Retrocomputing
  • Advent of Code on retro machines
  • DOS
• Reverse engineering
• Robotics
• SIMD programming
• Text editors
  • Emacs
  • Vim
• Unicode
• Version control tools
• Web programming
  • CSS
• Other
• Other lists

AI

• Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig

Machine Learning

• A Course in Machine Learning by Hal Daumé III

• A Visual Guide to Quantization: Demystifying the Compression of Large Language Models by Maarten Grootendorst

• Alice’s Adventures in a differentiable wonderland by Simone Scardapane

• An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani

• Applied Machine Learning for Tabular Data by Max Kuhn and Kjell Johnson

• Crash Course in Deep Learning (for Computer Graphics) by Jakub Boksansky [alternative link]

• Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control by Steven L. Brunton and J. Nathan Kutz [pdf]

• Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

• Deep Learning Course by François Fleuret

• Deep Learning: Foundations and Concepts by Chris Bishop with Hugh Bishop

• Deep Learning Interviews by Shlomo Kashani and Amir Ivry

• Deep Reinforcement Learning: Zero to Hero!

• Dive into Deep Learning

• Dive into Deep Learning Compiler

• Information Theory, Inference, and Learning Algorithms by David MacKay

• Introduction to ggml by Xuan Son NGUYEN, Georgi Gerganov and slaren

• Introduction to Machine Learning Interviews by Chip Huyen

• Learning Theory from First Principles by Francis Bach [pdf]

• Machine Learning Engineering Book by Andriy Burkov

• Machine Learning Engineering Open Book by Stas Bekman

• Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory by Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger

• Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

• Neural Networks: Zero to Hero - A course by Andrej Karpathy

• Neural Networks and Deep Learning by Michael Nielsen

• Physics-based Deep Learning by N. Thuerey, P. Holl, M. Mueller, P. Schnell, F. Trost, K. Um

• Practical Deep Learning for Coders

• Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy

• Probabilistic Machine Learning: Advanced Topics by Kevin Patrick Murphy

• Speech and Language Processing, 3rd edition by Daniel Jurafsky and James H. Martin

• The Elements of Differentiable Programming by Mathieu Blondel and Vincent Roulet

• The Engineer's Guide To Deep Learning by Hironobu Suzuki

• The Hundred Page Machine Learning Book by Andriy Burkov

• The Little Book of Deep Learning by François Fleuret

• Understanding Deep Learning by Simon J.D. Prince

Computer Games AI

• Game AI Pro by Steve Rabin

• Programming Starcraft AI by Peter Kis

Algorithms

• Algorithms by Jeff Erickson

• Algorithms and Data Structures by Kurt Mehlhorn and Peter Sanders [pdf]

• Algorithms for Decision Making by Mykel J. Kochenderfer, Tim A. Wheeler, and Kyle H. Wray

• Algorithms for Competitive Programming

• Algorithms for Optimization by Mykel J. Kochenderfer and Tim A. Wheeler

• Algorithms for Modern Hardware by Sergey Slotin

• Clever Algorithms: Nature-Inspired Programming Recipes by Jason Brownlee

• Collision Detection by Jeff Thompson

• Competitive Programmer's Handbook by Antti Laaksonen

• Data Structures for Data-Intensive Applications: Tradeoffs and Design Guidelines by Manos Athanassoulis , Stratos Idreos and Dennis Shasha [pdf]

• Exact String Matching Algorithms by Christian Charras and Thierry Lecroq

• Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan [pdf]

• How does B-tree make your queries fast? by Mateusz Kuźmik

• Introduction to Algorithms: A Creative Approach by Udi Manber [pdf]

• Kalman Filter from the Ground Up by Alex Becker

• Monte-Carlo Graph Search from First Principles by David J Wu

• Planning Algorithms by Steven M. LaValle

• Principles of Algorithmic Problem Solving by Johan Sannemo

• Problem Solving with Algorithms and Data Structures using Python by Brad Miller and David Ranum

• Purely Functional Data Structures by Chris Okasaki [pdf]

• Sequential and Parallel Data Structures and Algorithms: The Basic Toolbox by Peter Sanders, Kurt Mehlhorn, Martin Dietzfelbinger, and Roman Dementiev

• The Arcane Algorithm Archive

Art

• Pixel art articles and tutorials by Pedro Medeiros

Biology

• Introduction to Genomics for Engineers

• The Algorithmic Beauty of Plants by Przemyslaw Prusinkiewicz and Aristid Lindenmayer [pdf]

Command Line and Tools

• Driving Compilers by Fabien Sanglard

• Getting started with tmux by ittavern

• How I'm still not using GUIs: A guide to the terminal by Lucas Fernandes da Costa

• How is a binary executable organized? Let's explore it! by Julia Evans

• Learn Makefiles: With the tastiest examples by Chase Lambert

• NixOS & Flakes Book - An unofficial book for beginners by Ryan Yin

• The Definitive Guide To Docker

• Use Midnight Commander like a pro by Igor Klimer

Curl

• Curl Cookbook

• Curl Exercises by Julia Evans

• Mastering curl: interactive text guide by Anton Zhiyanov

Linux command line

• Effective Shell by Dave Kerr

• Linux command line for you and me by Kushal Das

• The Linux Command Handbook by Flavio Copes

• The Linux Command Line by William Shotts

Compilers and Interpreters

• Build Your Own Lisp by Daniel Holden

• Building the fastest Lua interpreter.. automatically! by Haoran Xu

  • Part 1
  • Part 2: Building a baseline JIT for Lua automatically

• Crafting Interpreters by Robert Nystrom

• Creating the Bolt Compiler by Mukul Rathi

• Essentials of Compilation: An Incremental Approach by Geremy G. Siek

• How Clang Compiles a Function by John Regehr

• How LLVM Optimizes a Function by John Regehr

• Let's Build a Compiler by Jack Crenshaw

• Let's make a Teeny Tiny compiler by Austin Z. Henley

  • Part 1
  • Part 2