ISBN 10: 1484285468 ISBN 13: 9781484285466
Librería: Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de America
EUR 31,14
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Añadir al carritoCondición: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.
Librería: Buchpark, Trebbin, Alemania
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Añadir al carritoCondición: Hervorragend. Zustand: Hervorragend | Seiten: 344 | Sprache: Englisch | Produktart: Bücher.
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
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Añadir al carritoPaperback or Softback. Condición: New. Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks 1.39. Book.
Librería: California Books, Miami, FL, Estados Unidos de America
EUR 51,71
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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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Añadir al carritoPAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
EUR 46,92
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Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 63,78
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days. 676.
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
EUR 55,00
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Librería: GreatBookPricesUK, Woodford Green, Reino Unido
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Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Librería: Chiron Media, Wallingford, Reino Unido
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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Añadir al carritoCondición: New. 1st ed. edition NO-PA16APR2015-KAP.
ISBN 10: 1484285468 ISBN 13: 9781484285466
Librería: Basi6 International, Irving, TX, Estados Unidos de America
EUR 31,14
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Añadir al carritoCondición: Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
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Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Añadir al carritoPaperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 676.
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 344 pages. 9.75x7.00x1.00 inches. In Stock. This item is printed on demand.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 69,54
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 decisionFurther, 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,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 LearnReview the different ways of making an AI model interpretable and explainableExamine the biasness and good ethical practices of AI modelsQuantify, visualize, and estimate reliability of AI modelsDesign frameworks to unbox the black-box modelsAssess the fairness of AI modelsUnderstand the building blocks of trust in AI modelsIncrease the level of AI adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products. 344 pp. Englisch.
Librería: moluna, Greven, Alemania
EUR 62,02
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Añadir al carritoKartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Intermediate-Advanced|Covers the core features of explainability and how to execute them using Python frameworksExplains XAI features to interpret supervised learning algorithms, NLP components and deep learning neural networksCovers biasne.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 73,88
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 decisionFurther, 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,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 LearnReview the different ways of making an AI model interpretable and explainableExamine the biasness and good ethical practices of AI modelsQuantify, visualize, and estimate reliability of AI modelsDesign frameworks to unbox the black-box modelsAssess the fairness of AI modelsUnderstand the building blocks of trust in AI modelsIncrease the level of AI adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
Librería: Majestic Books, Hounslow, Reino Unido
EUR 82,33
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 85,72
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