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Causal AI von Robert Osazuwa Ness Hardcover Buch-
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eBay-Artikelnr.:365748200667
Artikelmerkmale
- Artikelzustand
- ISBN-13
- 9781633439917
- Book Title
- Causal AI
- ISBN
- 9781633439917
Über dieses Produkt
Product Identifiers
Publisher
Manning Publications Co. LLC
ISBN-10
1633439917
ISBN-13
9781633439917
eBay Product ID (ePID)
11067494729
Product Key Features
Number of Pages
520 Pages
Publication Name
Causal Ai
Language
English
Publication Year
2025
Subject
Expert Systems, Intelligence (Ai) & Semantics, Data Processing, Programming Languages / Python
Type
Textbook
Subject Area
Computers
Format
Hardcover
Dimensions
Item Height
3.9 in
Item Weight
3.5 Oz
Item Length
3.9 in
Item Width
3.9 in
Additional Product Features
Synopsis
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., Build AI models that can reliably deliver causal inference. How do you know what might have happened, had you done things differently? Causal AI 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. Causal AI is a practical introduction to building AI models that can reason about causality. 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 Author Robert Osazuwa 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. Foreword by Lindsay Edwards . Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Traditional ML models can't answer causal questions like, "Why did that happen?" or, "What factors should I change to get a particular outcome?" This book blends advanced statistical methods, computational techniques, and new algorithms to create machine learning systems that automate the process of causal inference. About the book Causal AI introduces the tools, techniques, and algorithms of causal reasoning for machine learning. This unique book masterfully blends Bayesian and probabilistic approaches to causal inference with practical hands-on examples in Python. Along the way, you'll learn to integrate causal assumptions into deep learning architectures, including reinforcement learning and large language models. You'll also use PyTorch, Pyro, and other ML libraries to scale up causal inference. What's inside - End-to-end causal inference with DoWhy - Deep Bayesian causal generative AI models - A code-first tour of the do-calculus and Pearl's causal hierarchy - Code for fine-tuning causal large language models About the reader For data scientists and machine learning engineers. Examples in Python. About the author Robert Osazuwa Ness is an AI researcher at Microsoft Research and professor at Northeastern University. He is a contributor to open-source causal inference packages such as Python's DoWhy and R's bnlearn. Table of Contents Part 1 1 Why causal AI 2 A primer on probabilistic generative modeling Part 2 3 Building a causal graphical model 4 Testing the DAG with causal constraints 5 Connecting causality and deep learning Part 3 6 Structural causal models 7 Interventions and causal effects 8 Counterfactuals and parallel worlds 9 The general counterfactual inference algorithm 10 Identification and the causal hierarchy Part 4 11 Building a causal inference workflow 12 Causal decisions and reinforcement learning 13 Causality and large language models, 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.
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grandeagleretail
98,4% positive Bewertungen•2,8 Mio. Artikel verkauft
Angemeldet als gewerblicher Verkäufer
Verkäuferbewertungen (1.060.674)
- c***a (121)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufThe seller was very responsive and answered me on a timely matter. The product itself came in its packaging and was new, not used at all. The packaging was not beat up or anything, safely delivered to my mailbox. No mix ups and zero stress with delivery. The price for the product is completely understandable for the product. I really appreciate the seller and I am very happy to have purchased through this seller. Completely trustable!
- m***4 (1610)- Bewertung vom Käufer.Letzter MonatBestätigter KaufLeaving positive feedback because 1) item was packed well & arrived as described 2) seller did give partial refund when subsequent price dropped below org purchase price. 3) communication was quick However, there was a downside to this transaction -item listed as in-stock but ended up waiting nearly a month for them to get it from their distributer then ship it to me (bought June 29th, arrived around July 21). Auction said 12-15 days. Better clarity would have prevented lot of frustration
- w***i (880)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufAbsolutely Wonderful Seller!! Terrific Item As Described!!! Great Service and Communication!! Shipped In Waterproof Packaging!! I Received Item In About One Week!! Very Pleased With Seller! I Will Buy From This Seller Again!!