
全面的编程、数学和科学免费学习资源集锦
该项目整合了计算机编程、数学和科学领域的免费学习资源。内容涉及人工智能、机器学习、算法、图形学、网络编程等多个方向,从入门到高级主题一应俱全。提供的教材、教程和在线课程链接质量上乘,为自学者提供了系统学习的路径。资源列表涵盖广泛,从基础编程到前沿科技领域均有涉及。汇集了众多高质量的免费教程、电子书和在线课程,适合不同层次的学习者使用。内容持续更新,紧跟技术发展趋势,是编程爱好者和科技从业者的理想学习参考。
This is a list of links to different freely available learning resources about computer programming, math, and science.
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
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
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
Game AI Pro by Steve Rabin
Programming Starcraft AI by Peter Kis
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 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 Algorithmic Beauty of Plants by Przemyslaw Prusinkiewicz and Aristid Lindenmayer [pdf]
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
Use Midnight Commander like a pro by Igor Klimer
Curl Exercises by Julia Evans
Mastering curl: interactive text guide by Anton Zhiyanov
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
Build Your Own Lisp by Daniel Holden
Building the fastest Lua interpreter.. automatically! by Haoran Xu
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


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