Oscar Gonzalez retweetledi
BREAKING: MIT just mass released their Al library for free. (Links included)
I went through these and honestly... this is better than most paid courses I've seen.
Here's the full list of books:
Foundations
1. Foundations of Machine Learning Core algorithms explained. Theory meets practice.
2. Understanding Deep Learning Neural networks demystified. Visual explanations included.
3. Machine Learning Systems Production-ready architecture. System design principles.
Advanced Techniques
4. Algorithms for ML Computational thinking simplified. Decision-making frameworks.
5. Deep Learning The definitive textbook. Covers everything deeply.
Reinforcement Learning
6. RL Basics (Sutton & Barto) The classic. Agent training fundamentals.
7. Distributional RL Beyond expected rewards. Advanced theory.
8. Multi-Agent Systems Agents working together. Coordination and competition.
9. Long Game Al Strategic agent design. Future-focused thinking.
Ethics & Probability
10. Fairness in ML Bias detection. Responsible Al practices.
11. Probabilistic ML (Part 1 & 2)
Links: lnkd.in/gkuXuexa
Most people pay thousands for bootcamps that teach half of this.
Bookmark it. Start anywhere. Just start.
Repost for others Follow for more insights on Al Agents.
MIT's books on Al
Foundations
1. Foundations of Machine Learning - lnkd.in/gytjT5HC
2. Understanding Deep Learning - lnkd.in/dgcB68Qt
3. Machine Learning Systems - lnkd.in/dkiGZisg
Advanced Techniques
4. Algorithms for ML - algorithmsbook.com
5. Deep Learning - lnkd.in/g2efT6DK
Reinforcement Learning
6. RL Basics (Sutton & Barto) - lnkd.in/guxqxcZZ
7. Distributional RL - lnkd.in/d4eNP-pe
8. Multi-Agent Systems - marl-book.com
9. Long Game Al - lnkd.in/g-WtzvwX
Ethics & Probability
10. Fairness in ML - fairmlbook.org
11. Probabilistic ML (Part 1) - lnkd.in/g-isbdjj
12. Probabilistic ML (Part 2) - lnkd.in/gJE9fy4w
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