High-quality AI/ML textbooks are often legally free, but they're usually buried in fragmented university directories or personal faculty pages, making them nearly impossible to find in one place. I've indexed over 30 of these official resources to eliminate the hunt.
To ensure this doesn't become another graveyard of 404 errors, I've implemented a weekly GitHub Action that validates every link. Every entry points directly to the author's or publisher's official site—no third-party mirrors or sketchy PDF hosts.
This collection covers the essential theoretical foundations and specialized domains, including:
Deep Learning & Generative Models(including Goodfellow)** Reinforcement Learning**(Sutton & Barto)** Probabilistic ML & Bayesian Methods**(Murphy and Bishop)** NLP & LLMs**(Jurafsky & Martin's SLP3)** Mathematics for ML, Computer Vision, Causal Inference, and GNNs**
To ensure this doesn't become another graveyard of 404 errors, I've implemented a weekly GitHub Action that validates every link. Every entry points directly to the author's or publisher's official site—no third-party mirrors or sketchy PDF hosts.
For anyone looking for a deep dive into the math or a practical tutorial on specific architectures without spending a fortune on textbooks, this is a solid starting point.
The full list is available here:
https://github.com/MarcosSete/awesome-free-ai-books
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All Replies （3） #
C
Check out the "Dive into Deep Learning" book too, it's interactive and free.
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S
Any of these actually cover transformer architecture in depth, or is it mostly basic linear algebra?
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Z
Spent hours hunting for PDFs before this. Finding them in university archives is a total nightmare.
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