Name: Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reli (en Inglés)
Price: 48.52 USD
Availability: InStock
Author: Chakkirala, Aruna
ISBN: 9789348107572

portada Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reli (en Inglés)

Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reli (en Inglés)

Formato

Libro Físico

Editorial

Orange Education Pvt Ltd

Autor

Chakkirala, Aruna

Idioma

Inglés

N° páginas

274

Encuadernación

Tapa Blanda

Dimensiones

23.5 x 19.1 x 1.5 cm

ISBN13

9789348107572

Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reli (en Inglés)

Chakkirala, Aruna (Autor) · Orange Education Pvt Ltd · Tapa Blanda

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Reseña del libro "Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reli (en Inglés)"

Demystify AI Decisions and Master Interpretability and Explainability Today Book DescriptionInterpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You'll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you'll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals-powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you'll learn how to apply these techniques in real-world scenarios. By the end, you'll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards-giving you a competitive edge in the evolving AI landscape. Table of Contents1. Interpreting Interpretable Machine Learning2. Model Types and Interpretability Techniques3. Interpretability Taxonomy and Techniques4. Feature Effects Analysis with Plots5. Post-Hoc Methods6. Anchors and Counterfactuals7. Interpretability in Neural Networks8. Explainable Neural Networks9. Explainability in Transformers and Large Language Models10. Explainability and Responsible AI Index