Practical Explainable AI Using Python Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
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Sprache:Englisch
59,99 €
UVP
69,54 €
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Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
15.12.2021
Abbildungen
XVIII, 344 p. 194 illus., 144 illus. in color.
Verlag
ApressSeitenzahl
344
Maße (L/B/H)
25,4/17,8/1,9 cm
Gewicht
684 g
Auflage
1st ed.
Sprache
Englisch
ISBN
978-1-4842-7157-5
You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision
Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, youwill be introduced to model explainability for unstructured data, classification problems, and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.
What You'll Learn
- Review the different ways of making an AI model interpretable and explainable
- Examine the biasness and good ethical practices of AI models
- Quantify, visualize, and estimate reliability of AI models
- Design frameworks to unbox the black-box models
- Assess the fairness of AI models
- Understand the building blocks of trust in AI models
- Increase the level of AI adoption
Who This Book Is For
AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
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