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

Name: Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reli
Brand: Orange Education Pvt Ltd
SKU: 9789348107572
Price: 4092.0 INR
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by Orange Ava , Aruna Chakkirala

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ISBN13: 9789348107572
Binding: Paperback
Subject: N/A
Publisher: Orange Education Pvt Ltd
Publisher Imprint: Orange Education Pvt Ltd
Publication Date: April 15, 2025
Pages: 274
Original Price: USD 37.95
Language: English
Edition: N/A
Item Weight: 477 grams
BISAC Subject(s): Data Science / Neural Networks

About The Book

Demystify AI Decisions and Master Interpretability and Explainability Today

Book Description
Interpretability 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 Contents
1. Interpreting Interpretable Machine Learning
2. Model Types and Interpretability Techniques
3. Interpretability Taxonomy and Techniques
4. Feature Effects Analysis with Plots
5. Post-Hoc Methods
6. Anchors and Counterfactuals
7. Interpretability in Neural Networks
8. Explainable Neural Networks
9. Explainability in Transformers and Large Language Models
10. Explainability and Responsible AI
Index

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