Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Interpretability and Explainability in AI Using Python: Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reli

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

Chakkirala, Aruna

Paperback

General Computers

ISBN10: 9348107577
ISBN13: 9789348107572
Publisher: Orange Education Pvt Ltd
Published: Apr 15 2025
Pages: 274
Weight: 1.05
Height: 0.58 Width: 7.50 Depth: 9.25
Language: English
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.

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