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Applied Machine Learning Explainability by Bhattacharya Aditya (17 results)
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Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more (Paperback or Softback)
Published by Packt Publishing 7/29/2022, 2022
ISBN 10: 1803246154 ISBN 13: 9781803246154
Language: English
Paperback or Softback. Condition: New. Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more 1.16. Book.
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Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
Published by Packt Publishing 2022-07, 2022
ISBN 10: 1803246154 ISBN 13: 9781803246154
Language: English
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Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
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Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
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Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
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Applied Machine Learning Explainability Techniques
Published by Packt Publishing Limited, 2022
ISBN 10: 1803246154 ISBN 13: 9781803246154
Language: English
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Applied Machine Learning Explainability Techniques
Published by Packt Publishing Limited, 2022
ISBN 10: 1803246154 ISBN 13: 9781803246154
Language: English
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Applied Machine Learning Explainability Techniques: Best practices for making ML algorithms interpretable in the real-world applications using LIME, SHAP and others
Published by Packt Publishing Limited, 2022
ISBN 10: 1803246154 ISBN 13: 9781803246154
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features:Explore various explainability methods for designing robust and scalable explainable ML systems Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems Design user-centric explainable ML systems using guidelines provided for industrial applications Book...
