Explainable AI with Python

Explainable AI with Python: Demystifying the Black Box (2nd Edition)

Architect your transparency logic and master the high-performance protocols of modern AI engineering. Explainable AI with Python provides a definitive, intelligence-first roadmap to the most significant shift in machine learning since the digital revolution. Learn how to move beyond opaque "black box" models to high-velocity, interpretable discovery—bridging the gap between a complex neural network and a sophisticated, human-understandable ecosystem—ensuring your technical projects are resilient, scalable, and ready for the 2026 global technology landscape.

Note: This is a digital product and the download link will be sent to your email address immediately after payment.

What You Will Learn:

Foundations of Transparent Architecture: Master the core principles of the XAI landscape and the essential mechanics of making machine learning systems more explainable without sacrificing performance.

Modern LLM Workflows: Step-by-step guidance on explaining Large Language Models (LLMs) and multimodal systems to maintain peak operational integrity in generative AI environments.

Scalable Interpretability Patterns: Discover how to utilize Additive Models and model-agnostic methods to maintain the structural integrity and technical agility of your predictive analytics.

Strategic Security & Trust Integrity: Learn advanced techniques for maintaining information security through Adversarial Machine Learning and XAI, ensuring the technical agility of your ethical and responsible AI delivery.

Who This Book is For: This professional-grade guide is essential for Data Scientists, ML Engineers, and AI Researchers. It is an invaluable resource for any technical lead—including those building highly advanced, trust-dependent graduation projects like Smart Guard—aiming to master the structural integrity and technical agility required for modern, evidence-based software and system delivery.

Product Details:

Format: Digital PDF Download

Authors: Antonio Di Cecco; Leonida Gianfagna

Publisher: Springer (Released August 2025)

Edition: 2nd Edition

ISBN-13: 9783031922282

ISBN-10: 303192228X