MIT liberó su biblioteca de #IA & ML
100% FREE.
ENJOY!😍
𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
1. Foundations of Machine Learning
https://cs.nyu.edu/~mohri/mlbook/
The mathematical backbone of ML - algorithms, theory, and how models actually learn.
2. Understanding Deep Learning
https://udlbook.github.io/udlbook/
Neural networks explained visually and intuitively, from basics to modern architectures.
3. Deep Learning
https://www.deeplearningbook.org/
The definitive deep learning reference, written by the researchers who shaped the field.
4. Introduction to Machine Learning Systems
https://mlsysbook.ai/
How to design and build ML systems that work in production, not just in notebooks.
5. Algorithms for Optimization
https://algorithmsbook.com/optimization/
The math behind how models improve - gradient methods, search, and decision-making.
𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
6. Reinforcement Learning: An Introduction
http://incompleteideas.net/book/the-book.html
The classic RL textbook - how agents learn to make decisions through trial and reward.
7. Distributional Reinforcement Learning
https://www.distributional-rl.org/
Goes beyond average rewards to model the full distribution of outcomes.
8. Multi-Agent Reinforcement Learning
https://www.marl-book.com/
How multiple AI agents learn, compete, and cooperate in shared environments.
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗠𝗟
9. Probabilistic Machine Learning: An Introduction
https://probml.github.io/pml-book/book1.html
ML through the lens of probability - uncertainty, inference, and Bayesian thinking.
10. Probabilistic Machine Learning: Advanced Topics
https://probml.github.io/pml-book/book2.html
Deep dives into probabilistic models, approximate inference, and generative methods.
𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
11. Agents in the Long Game of AI
https://direct.mit.edu/books/oa-monograph/5779/Agents-in-the-Long-Game-of-AIComputational
How to build AI agents that are trustworthy, hybrid, and designed for long-term reliability.
12. Fairness and Machine Learning
https://fairmlboo