Interpretable AI Building Explainable Machine Learning Systems(1st Edition)
Authors:
Ajay Thampi
In Stock: 2 Left
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Solution Manual Includes
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$9.99
Book details
ISBN: 161729764X, 978-1617297649
Book publisher: Manning
Book Price $0 : Ajay Thampi’s 'Interpretable AI: Building Explainable Machine Learning Systems' is an essential guide for understanding the increasing necessity of transparency in AI solutions. The book thoroughly explores various methodologies and techniques to make machine learning systems more interpretable and reliable, a crucial aspect as AI technologies become deeply embedded in critical decisions across sectors like healthcare, finance, and transportation. Thampi delves into the importance of explainability in AI, outlining how explainable systems enhance trust and accountability by allowing their inner workings to be understood and verified by humans. Key technical terms such as SHAP values, LIME (Local Interpretable Model-agnostic Explanations), and model transparency techniques are explained in detail, providing readers with practical insights into implementing these methodologies. The discussion extends to advanced topics like feature importance, rule-based models, and the balance between model accuracy and interpretability. The book, while technical, is structured to serve as both an answer key and a solution manual for practitioners seeking to bridge the gap between complex machine learning systems and user comprehension. Its well-organized table of contents allows for easy navigation through the wealth of knowledge offered. 'Interpretable AI' has been widely praised in academic circles and by industry experts for its clear writing and comprehensive coverage of a crucial aspect of contemporary AI systems. As AI continues to evolve, this book stands as a pivotal resource for ensuring these systems remain transparent and trustworthy. This book summary includes a cheap solution manual, making problem-solving accessible to everyone.
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