Causal AI
Seiten
2025
Manning Publications (Verlag)
978-1-63343-991-7 (ISBN)
Manning Publications (Verlag)
978-1-63343-991-7 (ISBN)
Causal AI is a practical introduction to building AI models that can reason about causality. Robert Ness' clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes.
How do you know what might have happened, had you done things differently? Causal machine learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions.
In Causal AI you will learn how to:
Build causal reinforcement learning algorithms
Implement causal inference with modern probabilistic machine tools such as PyTorch and Pyro
Compare and contrast statistical and econometric methods for causal inference
Set up algorithms for attribution, credit assignment, and explanation
Convert domain expertise into explainable causal models
Causal AI is a practical introduction to building AI models that can reason about causality. Author Robert Ness, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes. About the technology: Causal machine learning is a major milestone in machine learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars.
How do you know what might have happened, had you done things differently? Causal machine learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions.
In Causal AI you will learn how to:
Build causal reinforcement learning algorithms
Implement causal inference with modern probabilistic machine tools such as PyTorch and Pyro
Compare and contrast statistical and econometric methods for causal inference
Set up algorithms for attribution, credit assignment, and explanation
Convert domain expertise into explainable causal models
Causal AI is a practical introduction to building AI models that can reason about causality. Author Robert Ness, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes. About the technology: Causal machine learning is a major milestone in machine learning, allowing AI models to make accurate predictions based on causes rather than just correlations. Causal techniques help you make models that are more robust, explainable, and fair, and have a wide range of applications, from improving recommendation engines to perfecting self-driving cars.
Robert Ness is a leading researcher in causal AI at Microsoft Research. He is a contributor to open-source causal inference packages such as Python's DoWhy and R's bnlearn.
| Erscheinungsdatum | 07.03.2025 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 100 x 100 mm |
| Gewicht | 100 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-63343-991-7 / 1633439917 |
| ISBN-13 | 978-1-63343-991-7 / 9781633439917 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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