Practical Explainable AI Using Python : Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks Paperback - 2021
by Pradeepta Mishra
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- Paperback
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Details
- Title Practical Explainable AI Using Python : Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
- Author Pradeepta Mishra
- Binding Paperback
- Condition New
- Pages 344
- Volumes 1
- Language ENG
- Publisher Apress
- Publication date 2021-12-15
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # ria9781484271575_inp
- ISBN 9781484271575 / 1484271572
- Weight 1.39 lbs (0.63 kg)
- Dimensions 10 x 7 x 0.75 in (25.40 x 17.78 x 1.91 cm)
- Category Computers - General Information
- Quantity available 626
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From the publisher
From the rear cover
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, youwill be introduced to model explainability for unstructured data 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.
You will:
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 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.
You will:
- 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
