Practical Explainable AI Using Python : Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
1st ed.
Book Details
Format
Paperback / Softback
ISBN-10
1484271572
ISBN-13
9781484271575
Edition
1st ed.
Publisher
APress
Imprint
APress
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Dec 15th, 2021
Print length
344 Pages
Ksh 9,900.00
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0 in stock
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Quality
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Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.
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, you will 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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