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Apr. 9th, 2025 11:20 am![[personal profile]](https://www.dreamwidth.org/img/silk/identity/user.png){kind=small}
shiningfractalPerfeito. Aqui está a lista reescrita, limpa e organizada:
Área Prioritária: Inteligência Artificial — 30 Livros Importantes Ainda Sem Tradução no Brasil (ou de circulação limitada)
1. Artificial Intelligence: A New Synthesis — Nils J. Nilsson
2. Pattern Recognition and Machine Learning — Christopher M. Bishop
3. Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville
4. Reinforcement Learning: An Introduction — Richard S. Sutton, Andrew G. Barto
5. Artificial Intelligence: Foundations of Computational Agents — David L. Poole, Alan K. Mackworth
6. Probabilistic Graphical Models: Principles and Techniques — Daphne Koller, Nir Friedman
7. Bayesian Reasoning and Machine Learning — David Barber
8. Machine Learning: A Probabilistic Perspective — Kevin P. Murphy
9. Computer Vision: Algorithms and Applications — Richard Szeliski
10. Natural Language Processing with Python — Steven Bird, Ewan Klein, Edward Loper
11. Speech and Language Processing — Daniel Jurafsky, James H. Martin
12. Understanding Machine Learning: From Theory to Algorithms — Shai Shalev-Shwartz, Shai Ben-David
13. The Elements of Statistical Learning — Trevor Hastie, Robert Tibshirani, Jerome Friedman
14. Information Theory, Inference, and Learning Algorithms — David J.C. MacKay
15. Artificial Intelligence: A Guide to Intelligent Systems — Michael Negnevitsky
16. Data Mining: Practical Machine Learning Tools and Techniques — Ian H. Witten, Eibe Frank, Mark A. Hall
17. Machine Learning Yearning — Andrew Ng
18. Artificial Intelligence: Structures and Strategies for Complex Problem Solving — George F. Luger
19. Introduction to the Theory of Neural Computation — John A. Hertz, Anders Krogh, Richard G. Palmer
20. Learning Deep Architectures for AI — Yoshua Bengio
21. Neural Networks and Deep Learning: A Textbook — Charu C. Aggarwal
22. Introduction to Machine Learning — Ethem Alpaydin
23. Fundamentals of Machine Learning for Predictive Data Analytics — John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
24. Probabilistic Machine Learning: An Introduction — Kevin P. Murphy
25. Graph Representation Learning — William L. Hamilton
26. Automated Machine Learning: Methods, Systems, Challenges — Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
27. Machine Learning for Healthcare — Kevin P. Murphy
28. Deep Learning for Computer Vision — Rajalingappaa Shanmugamani
29. Applied Artificial Intelligence: A Handbook for Business Leaders — Mariya Yao, Adelyn Zhou, Marlene Jia
30. AI: A Very Short Introduction — Margaret A. Boden
Quer que eu monte um projeto detalhado só para essa área (Inteligência Artificial) com foco em tradução, formação de tradutores e bibliotecas digitais? Posso fazer uma proposta de ONG ou iniciativa estadual específica. Quer seguir nesse foco?
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Área Prioritária: Inteligência Artificial — 30 Livros Importantes Ainda Sem Tradução no Brasil (ou de circulação limitada)
1. Artificial Intelligence: A New Synthesis — Nils J. Nilsson
2. Pattern Recognition and Machine Learning — Christopher M. Bishop
3. Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville
4. Reinforcement Learning: An Introduction — Richard S. Sutton, Andrew G. Barto
5. Artificial Intelligence: Foundations of Computational Agents — David L. Poole, Alan K. Mackworth
6. Probabilistic Graphical Models: Principles and Techniques — Daphne Koller, Nir Friedman
7. Bayesian Reasoning and Machine Learning — David Barber
8. Machine Learning: A Probabilistic Perspective — Kevin P. Murphy
9. Computer Vision: Algorithms and Applications — Richard Szeliski
10. Natural Language Processing with Python — Steven Bird, Ewan Klein, Edward Loper
11. Speech and Language Processing — Daniel Jurafsky, James H. Martin
12. Understanding Machine Learning: From Theory to Algorithms — Shai Shalev-Shwartz, Shai Ben-David
13. The Elements of Statistical Learning — Trevor Hastie, Robert Tibshirani, Jerome Friedman
14. Information Theory, Inference, and Learning Algorithms — David J.C. MacKay
15. Artificial Intelligence: A Guide to Intelligent Systems — Michael Negnevitsky
16. Data Mining: Practical Machine Learning Tools and Techniques — Ian H. Witten, Eibe Frank, Mark A. Hall
17. Machine Learning Yearning — Andrew Ng
18. Artificial Intelligence: Structures and Strategies for Complex Problem Solving — George F. Luger
19. Introduction to the Theory of Neural Computation — John A. Hertz, Anders Krogh, Richard G. Palmer
20. Learning Deep Architectures for AI — Yoshua Bengio
21. Neural Networks and Deep Learning: A Textbook — Charu C. Aggarwal
22. Introduction to Machine Learning — Ethem Alpaydin
23. Fundamentals of Machine Learning for Predictive Data Analytics — John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
24. Probabilistic Machine Learning: An Introduction — Kevin P. Murphy
25. Graph Representation Learning — William L. Hamilton
26. Automated Machine Learning: Methods, Systems, Challenges — Frank Hutter, Lars Kotthoff, Joaquin Vanschoren
27. Machine Learning for Healthcare — Kevin P. Murphy
28. Deep Learning for Computer Vision — Rajalingappaa Shanmugamani
29. Applied Artificial Intelligence: A Handbook for Business Leaders — Mariya Yao, Adelyn Zhou, Marlene Jia
30. AI: A Very Short Introduction — Margaret A. Boden
Quer que eu monte um projeto detalhado só para essa área (Inteligência Artificial) com foco em tradução, formação de tradutores e bibliotecas digitais? Posso fazer uma proposta de ONG ou iniciativa estadual específica. Quer seguir nesse foco?
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