Everything and the Kitchen Sink for AI Self-Study

What it does This repository is a curated index of AI learning materials. It gathers more than a hundred cheat-sheets, free online textbooks, university courses, video lectures, research papers, datasets, and tools into a single flat list. Think of it as a syllabus assembled from scattered corners of the internet.

The interesting bit The cheat-sheets cover the full stack—from Python, SQL, and math foundations up through machine learning and deep learning—so the repo doubles as a quick-reference companion, not just a reading list. The curator also treats classic papers and university courses as first-class citizens, giving the collection a syllabus-like shape rather than a random link-dump feel.

Key highlights

• 100+ cheat-sheets spanning AI, data science, Python, R, SQL, and the underlying math
• Curated links to classic textbooks including Deep Learning by Goodfellow, Bengio, and Courville, and Artificial Intelligence: A Modern Approach
• Course index covering Stanford, MIT, CMU, Berkeley, NYU, and Fast.ai
• Paper collection touching on ImageNet, autoencoders, RNNs, LSTMs, GRUs, and neural Turing machines
• Datasets, conference pointers, and framework references rounded up in one place

Caveats

• This is strictly a link directory—no original code, tools, or exercises live in the repo
• Annotations are minimal; you get a title and URL, not a review or difficulty rating
• Many cheat-sheets are stored in a separate companion repository, so navigation is split across two repos

Verdict Worth bookmarking if you’re building a self-directed AI curriculum and want a single starting point. Look elsewhere if you need runnable code, interactive tutorials, or structured lesson plans.