Practical explainable AI using Python: artificial intelligence model explanations using Python-based libraries, extensions, and frameworks
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...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berkeley, CA
Apress
[2022]
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| Ausgabe: | 1st ed |
| Schlagworte: | |
| Zusammenfassung: | 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, 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.What You'll Learn - 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 adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products |
| Beschreibung: | xviii, 344 Seiten Illustrationen, Diagramme 692 grams |
| ISBN: | 9781484271575 |
Internformat
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Datensatz im Suchindex
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| id | DE-604.BV048218269 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:50:02Z |
| indexdate | 2024-07-10T09:32:20Z |
| institution | BVB |
| isbn | 9781484271575 |
| language | English |
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| physical | xviii, 344 Seiten Illustrationen, Diagramme 692 grams |
| publishDate | 2022 |
| publishDateSearch | 2022 |
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| publisher | Apress |
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| spelling | Mishra, Pradeepta aut Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra 1st ed Berkeley, CA Apress [2022] © 2022 xviii, 344 Seiten Illustrationen, Diagramme 692 grams txt rdacontent n rdamedia nc rdacarrier 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, 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.What You'll Learn - 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 adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products bicssc bisacsh Python (Computer program language) Artificial intelligence Hardcover, Softcover / Informatik, EDV/Informatik Erscheint auch als Online-Ausgabe 978-1-4842-7158-2 |
| spellingShingle | Mishra, Pradeepta Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks bicssc bisacsh Python (Computer program language) Artificial intelligence |
| title | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| title_auth | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| title_exact_search | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| title_exact_search_txtP | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| title_full | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra |
| title_fullStr | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra |
| title_full_unstemmed | Practical explainable AI using Python artificial intelligence model explanations using Python-based libraries, extensions, and frameworks Pradeepta Mishra |
| title_short | Practical explainable AI using Python |
| title_sort | practical explainable ai using python artificial intelligence model explanations using python based libraries extensions and frameworks |
| title_sub | artificial intelligence model explanations using Python-based libraries, extensions, and frameworks |
| topic | bicssc bisacsh Python (Computer program language) Artificial intelligence |
| topic_facet | bicssc bisacsh Python (Computer program language) Artificial intelligence |
| work_keys_str_mv | AT mishrapradeepta practicalexplainableaiusingpythonartificialintelligencemodelexplanationsusingpythonbasedlibrariesextensionsandframeworks |