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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...
| Call Number: | Libro Electrónico |
|---|---|
| Main Author: | |
| Format: | Electronic eBook |
| Language: | Inglés |
| Published: |
Berkeley, CA :
Apress L.P.,
2022.
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| Subjects: | |
| Online Access: | Texto completo (Requiere registro previo con correo institucional) |
Table of Contents:
- 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.