商品描述
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:
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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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. About the book Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You'll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you. About the reader For data scientists and machine learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python. About the author 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.
- 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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. About the book Causal AI teaches you how to build machine learning and deep learning models that implement causal reasoning. Discover why leading AI engineers are so excited by causal reasoning, and develop a high-level understanding of this next major trend in AI. New techniques are demonstrated with example models for solving industry-relevant problems. You'll learn about causality for recommendations; causal modeling of online conversions; and uplift, attribution, and churn modeling. Each technique is tested against a common set of problems, data, and Python libraries, so you can compare and contrast which will work best for you. About the reader For data scientists and machine learning engineers. A familiarity with probability and statistics will be helpful, but not essential, to begin this guide. Examples in Python. About the author 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.
商品描述(中文翻譯)
如何知道如果你以不同的方式行事，可能會發生什麼？因果機器學習為你提供了根據因果關係而非純粹相關性進行預測和控制結果所需的洞察，讓你能夠進行精確且及時的干預。
在Causal AI中，你將學習如何：- 建立因果強化學習算法
- 使用現代概率機器工具如PyTorch和Pyro實現因果推斷
- 比較和對比因果推斷的統計和計量經濟學方法
- 設置歸因、信用分配和解釋的算法
- 將領域專業知識轉換為可解釋的因果模型
Causal AI是一本實用的入門書，介紹如何構建能夠推理因果關係的AI模型。作者Robert Ness是微軟研究院因果AI的領先研究者，他將其獨特的專業知識帶入這本前沿指南中。他清晰的以代碼為先的方式解釋了因果機器學習中隱藏在學術論文中的重要細節。你所學到的一切都可以輕鬆有效地應用於行業挑戰，從構建可解釋的因果模型到預測反事實結果。 購買印刷版書籍可獲得Manning Publications提供的免費PDF和ePub格式電子書。 關於技術 因果機器學習是機器學習的一個重要里程碑，允許AI模型根據原因而非僅僅是相關性進行準確預測。因果技術幫助你構建更穩健、可解釋和公平的模型，並具有廣泛的應用，從改善推薦引擎到完善自駕車。 關於本書 Causal AI教你如何構建實現因果推理的機器學習和深度學習模型。了解為什麼領先的AI工程師對因果推理如此興奮，並發展對這一AI下一個主要趨勢的高層次理解。新技術通過示例模型展示，解決與行業相關的問題。你將學習到推薦的因果性；在線轉換的因果建模；以及提升、歸因和流失建模。每種技術都針對一組共同的問題、數據和Python庫進行測試，讓你可以比較和對比哪種方法最適合你。 關於讀者 針對數據科學家和機器學習工程師。對概率和統計的熟悉將有助於開始本指南，但不是必需的。示例使用Python。 關於作者 Robert Ness是微軟研究院因果AI的領先研究者。他是開源因果推斷包的貢獻者，如Python的DoWhy和R的bnlearn。
作者簡介
Robert Osazuwa 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.
作者簡介(中文翻譯)
羅伯特 奧薩祖瓦 內斯 是微軟研究院的因果人工智慧領域的領先研究者。他是開源因果推斷套件的貢獻者，例如 Python 的 DoWhy 和 R 的 bnlearn。
