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In den WarenkorbZustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher.
ISBN 10: 1484285468 ISBN 13: 9781484285466
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
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In den WarenkorbZustand: 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.
ISBN 10: 1484285468 ISBN 13: 9781484285466
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In den WarenkorbZustand: 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.
Anbieter: BargainBookStores, Grand Rapids, MI, USA
EUR 45,21
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In den WarenkorbPaperback or Softback. Zustand: New. Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks 1.39. Book.
Anbieter: California Books, Miami, FL, USA
EUR 51,10
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Anbieter: GreatBookPrices, Columbia, MD, USA
EUR 42,87
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Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
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In den WarenkorbPAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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In den WarenkorbZustand: New.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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In den WarenkorbZustand: New. 1st ed. edition NO-PA16APR2015-KAP.
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Anbieter: moluna, Greven, Deutschland
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In den WarenkorbKartoniert / Broschiert. Zustand: 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.
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
EUR 69,54
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In den WarenkorbTaschenbuch. Zustand: 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.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 65,42
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In den WarenkorbPaperback. Zustand: Brand New. 344 pages. 9.75x7.00x1.00 inches. In Stock. This item is printed on demand.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
EUR 75,37
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In den WarenkorbTaschenbuch. Zustand: 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.
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
EUR 81,64
Währung umrechnenAnzahl: 4 verfügbar
In den WarenkorbZustand: New. PRINT ON DEMAND.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 79,65
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In den WarenkorbZustand: New. Print on Demand.