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: https://t.co/syzvgxbVcL
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 - https://t.co/w4D7wpbf5f
2. Understanding Deep Learning - https://t.co/CjcKpTemmV
3. Machine Learning Systems - https://t.co/t920IxXNtQ
Advanced Techniques
4. Algorithms for ML - https://t.co/FNsUoPEPpa
5. Deep Learning - https://t.co/LokT3uJiaQ
Reinforcement Learning
6. RL Basics (Sutton & Barto) - https://t.co/QfveRFM8tP
7. Distributional RL - https://t.co/uNwPoDuKdf
8. Multi-Agent Systems - https://t.co/faE2R5ORGt
9. Long Game Al - https://t.co/7MFrdPlQor
Ethics & Probability
10. Fairness in ML - https://t.co/ntNTV6du5e
11. Probabilistic ML (Part 1) - https://t.co/1xrVgA5C3h
12. Probabilistic ML (Part 2) - https://t.co/cMC0mOfOD7
For learning Machine Learning with actual projects, checkout this Repo.
920 open-source projects with a total of 4.7M stars grouped into 34 categories.
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All rejected papers share one common flaw: a weak discussion section.
I know because I used to write them too.
If you're stuck on the discussion section,
wondering how to tie it all together,
this is for you:
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