A book-in-progress about the Linux kernel and its insides.
- linux-kernel
- linux-insides
- linux
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A book-in-progress about the Linux kernel and its insides.
500 Lines or Less
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
TranslatedChinese translation of 《Designing Data-Intensive Application》 (DDIA), 1st edition / 2nd edition
Machine Learning Engineering Open Book
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
A free book that talks about design patterns/techniques used while developing with React.
:blue_book: A guided handbook on how to use Babel and how to create plugins for Babel.
Approaching (Almost) Any Machine Learning Problem
A book about the internals of the Go programming language.
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
The canonical reference for Astrid: kernel, capsules, host ABI, IPC, and the security model.
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Step-by-step guide through the abstract and complex universe of Fragment Shaders.
Matlab code of machine learning algorithms in book PRML
Translated:book: Source code for the book 《Unity Shader入门精要》 (Unity Shader Essentials)
📗📒 (PT-Br translation) JS Book Series.
"Probabilistic Machine Learning" - a book series by Kevin Murphy
Book about interpretable machine learning
🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.
Translated"Interpretable Machine Learning -- A Guide to Understanding the Interpretability of Black-Box Models", the Chinese edition of the book "Interpretable Machine Learning"
Markdown source for Michael Abrash's Graphics Programming Black Book