Textbooks and toy environments for the self-taught ML engineer

What it does This repository is essentially a well-organized syllabus and index. It collects links to foundational AI textbooks—covering mathematics, machine learning, deep learning, data mining, and even philosophy of AI—alongside a directory of reinforcement-learning training environments like OpenAI Gym, StarCraft II bots, and Minecraft-based Malmö. Because GitHub frowns on large files, the actual PDFs live on Yandex.Disk. The maintainer also marks commercial titles separately, pointing those to publishers rather than file shares.

The interesting bit The scope is admirably stubborn: it refuses to stay in one lane, bundling linear algebra primers with quantum-computing basics, LLM reading lists, and a Dota 2 scripting API. That breadth makes it a decent one-stop starting point for anyone trying to backfill the math and theory they skipped in college.

Key highlights

• Curated links to classic texts (Bishop’s Pattern Recognition and Machine Learning, Goodfellow’s Deep Learning, Russell & Norvig’s AIMA) and Chinese translations.
• A Training ground section listing two dozen RL environments, from Atari clones to StarCraft and Doom wrappers.
• Explicitly labels commercial books versus freely hosted PDFs.
• Includes quantum computing and quantum-AI sections, plus distributed-training resources.
• A math-symbols cheat sheet for decoding notation-heavy chapters.

Caveats

• All PDFs are stored on Yandex.Disk, so link rot is a real risk.
• The README explicitly warns the material is “only used for learning, do not use in business,” which is not a substitute for actual licensing clarity.
• Most entries are external links; the repo itself is primarily a curated index rather than a collection of runnable code or notebooks.

Verdict Worth bookmarking if you are self-studying AI and need a structured syllabus with both theory and RL sandboxes. Skip it if you are looking for original code, runnable notebooks, or guaranteed-stable downloads.