Practical explainable AI using Python : artificial intelligence model explanations using Python-based libraries, extensions, and frameworks /

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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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mishra, Pradeepta
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress L.P., 2022.
Temas:
Python (Computer program language)
Artificial intelligence > Data processing.
Application software > Development.
Python (Langage de programmation)
Intelligence artificielle > Informatique.
Logiciels d'application > Développement.
Application software > Development
Artificial intelligence > Data processing
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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