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Kimi-K3
Kimi K3 is an open-weight, 2.8T-parameter native multimodal agentic model with a 1M-token context window, designed for frontier coding, knowledge work, and reasoning tasks.
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FR-AI / ANALYSISWhy it is worth attention
It aggregates authoritative, up-to-date textbooks across 14 AI subfields—all from official sources—ensuring both legality and the latest versions, while actively encouraging community contributions.
Who it is for
- Students and researchers in AI/ML
- Self-taught learners seeking structured resources
- Practitioners needing reference handbooks
- Educators compiling course reading lists
Use cases
- Finding free, trusted textbooks for deep learning, NLP, or RL courses
- Building a personal library of open-access AI references
- Discovering active book drafts that are frequently updated (e.g., SLP3)
- Verifying official sources for widely cited ML texts
Strengths
- Strictly uses official links (no pirated or third-party copies) – respects licenses and provides the most current editions
- Broad coverage spanning 14 categories from mathematics to AI safety
- Community-driven with clear contribution guidelines and automatic link checking
- Includes several continuously updated books (e.g., Deep Learning, SLP3, The Little Book of Deep Learning)
Considerations
- Only contains links to books; no original content or summaries
- Some books listed are older (e.g., 1999, 2003) and may not reflect the latest research
- Limited to textual books – excludes courses, papers, or interactive resources
README quick start
📚 Awesome Free AI Books
A curated list of free, legitimately accessible books on Deep Learning, Reinforcement Learning, Probabilistic Machine Learning, NLP/LLMs, and mathematical foundations — all made officially available by their own authors or publishers.
Every link points to an official source. This repository doesn't host any files — it only organizes and catalogs what authors already offer for free. See why at the bottom.
Table of Contents
- 🧠 Deep Learning
- 🎮 Reinforcement Learning
- 📊 Probabilistic / Bayesian
- 📈 Classical Machine Learning / Statistics
- 🗣️ NLP / LLMs
- 🧮 Mathematics for ML
- 🖥️ ML Systems / Infrastructure
- 👁️ Computer Vision
- 🎨 Generative Models (Diffusion, GANs, VAEs)
- 🔗 Causal Inference
- 🕸️ Graph Neural Networks
- 🛡️ AI Safety / Alignment
- 🎓 Advanced RL / Theory
- How to Contribute
- Why Official Links Only
🧠 Deep Learning
| Book | Author(s) | Year | Official Link |
|---|---|---|---|
| Deep Learning | Ian Goodfellow, Yoshua Bengio, Aaron Courville (MIT Press) | 2016 | deeplearningbook.org |
| Understanding Deep Learning | Simon J.D. Prince | 2023 | udlbook.github.io |
| Deep Learning: Foundations and Concepts | Christopher Bishop, Hugh Bishop | 2024 | bishopbook.com |
| Dive into Deep Learning (D2L) | Zhang, Lipton, Li, Smola | 2023 | d2l.ai |
| The Little Book of Deep Learning | François Fleuret | 2023 (updated 2026) | fleuret.org |
| Neural Networks and Deep Learning | Michael Nielsen | 2015 | neuralnetworksanddeeplearning.com |
| A Brief Introduction to Neural Networks | David Kriesel | — | [ |
Description
A curated list of free, legitimate AI/ML books
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