Produktbild: Practical Explainable AI Using Python
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Practical Explainable AI Using Python Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.12.2021

Abbildungen

XVIII, 344 p. 194 illus., 144 illus. in color.

Verlag

Apress

Seitenzahl

344

Maße (L/B/H)

25,4/17,8/1,9 cm

Gewicht

684 g

Auflage

1st ed.

Sprache

Englisch

ISBN

978-1-4842-7157-5

Beschreibung

Rezension

“Practical explainable AI using Python combines textbook and cookbook elements. It provides explanations of concepts along with practical examples and exercises. … this book offers a comprehensive foundation that will remain relevant for some time. However, readers should supplement their knowledge with the latest research in order to stay up to date in this dynamic field.” (Gulustan Dogan, Computing Reviews, August 21, 2023)






“While the book presents just fundamental aspects, I find this to be a great advantage. Indeed, even the layperson to AI/ML can use this work: the author starts with the most basic definitions and models, and then provides software examples … . This way a very broad readership is possible, since more advanced parts of the chapters will be interesting even for specialists in AI/ML who would like to increase their expertise in the title topic.” (Piotr Cholda, Computing Reviews, April 17, 2023)

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.12.2021

Abbildungen

XVIII, 344 p. 194 illus., 144 illus. in color.

Verlag

Apress

Seitenzahl

344

Maße (L/B/H)

25,4/17,8/1,9 cm

Gewicht

684 g

Auflage

1st ed.

Sprache

Englisch

ISBN

978-1-4842-7157-5

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Practical Explainable AI Using Python
  • Chapter 1:  Introduction to Model Explainability and Interpretability.- Chapter 2:  AI Ethics, Biasness and Reliability.- Chapter 3: Model Explainability for Linear Models Using XAI Components.- Chapter 4: Model Explainability for Non-Linear Models using XAI Components.- Chapter 5: Model Explainability for Ensemble Models Using XAI Components.- Chapter 6: Model Explainability for Time Series Models using XAI Components.- Chapter 7: Model Explainability for Natural Language Processing using XAI Components.- Chapter 8: AI Model Fairness Using What-If Scenario.- Chapter 9: Model Explainability for Deep Neural Network Models.- Chapter 10: Counterfactual Explanations for XAI models.- Chapter 11: Contrastive Explanation for Machine Learning.- Chapter 12: Model-Agnostic Explanations By Identifying Prediction Invariance.- Chapter 13: Model Explainability for Rule based Expert System.- Chapter 14: Model Explainability for Computer Vision.