Explainable Deep Learning AI 1st Edition

Capture the future of transparent AI with Explainable Deep Learning AI (1st Edition), a definitive guide by Jenny Benois-Pineau, Romain Bourqui, Dragutin Petkovic, and Georges Quenot. This authoritative volume demystifies the black box of deep learning, offering practitioners and researchers clear pathways to interpretability, accountability, and trust.

Discover rigorous yet accessible explanations of core concepts—from model-agnostic methods and saliency maps to concept-based explanations and counterfactual reasoning—paired with practical evaluation metrics. Rich case studies emphasize real-world applications in computer vision, healthcare, autonomous systems, and multimedia analytics, showing how explainable AI drives safer, fairer, and more compliant deployments across industries.

Engineered for data scientists, ML engineers, graduate students, and decision-makers, the book blends theoretical foundations with hands-on insights, bridging academic research and industrial practice. Authors combine multidisciplinary expertise to present reproducible workflows, best practices for human-centered explanations, and guidance on regulatory and ethical considerations.

Whether you’re building explainable models in Europe, North America, Asia, or beyond, this edition equips you with the tools to enhance model transparency, improve stakeholder communication, and accelerate adoption of AI solutions. Concise, engaging, and practical, it’s an essential resource for anyone committed to responsible AI.

Add Explainable Deep Learning AI (1st Edition) to your library today — empower your projects with clarity, confidence, and cutting-edge interpretability techniques.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.