Finding high-quality, legally free textbooks usually involves digging through outdated university syllabi or obscure personal blogs. While the information is out there, it's fragmented. I've found a curated index that aggregates over 30 of these resources into one place, focusing on official links rather than sketchy PDF mirrors.
Beyond the basics, it hits specialized niches like Causal Inference, GNNs, and AI Safety—which is critical if you're trying to understand the boundaries of LLM behavior and red-teaming.
The collection covers the heavy hitters you actually need for a serious AI workflow. We're talking about the "bibles" of the field:
Deep Learning: Goodfellow's foundational text.Reinforcement Learning: Sutton & Barto.Probabilistic ML: Murphy and Bishop’s latest work.NLP & LLMs: Jurafsky & Martin’s SLP3 drafts.
Beyond the basics, it hits specialized niches like Causal Inference, GNNs, and AI Safety—which is critical if you're trying to understand the boundaries of LLM behavior and red-teaming.
What makes this repo actually useful is the maintenance; there's a weekly GitHub Action that pings the links to ensure they aren't dead. If you're building a roadmap from scratch or need a theoretical anchor for your prompt engineering experiments, this is a much cleaner starting point than a random Google search.
The repository is hosted here:
https://github.com/MarcosSete/awesome-free-ai-books
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All Replies （3） #
J
Don't forget about the Deep Learning book by Ian Goodfellow, it's a classic and free online.
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S
Any of these actually explain transformers without making my brain leak out of my ears?
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A
I usually pair these with Kaggle notebooks to actually practice the theory in real-time.
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