English | 2021 | ISBN: 1484271572 | 356 pages | True (PDF,EPUB) | 41.09 MB
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 b 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 -series models used in AI. The book will also cover topics rag 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.
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
AI eeers, data scientists, and software developers involved in driving AI projects/ AI products.
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