Deep Learning and XAI Techniques for Anomaly Detection: Integrate the theory and practice of deep anomaly explainability

English | 2023 | ISBN: 978-1804617755 | 218 Pages | PDF, EPUB | 30 MB

Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guide

Key Features

• Build auditable XAI models for replicability and regulatory compliance
• Derive critical insights from transparent anomaly detection models
• Strike the right balance between model accuracy and interpretability

Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance.

Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that’ll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you’ll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis.

This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you’ll get equipped with XAI and anomaly detection knowledge that’ll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you’ll learn how to quantify and assess their explainability.

By the end of this deep learning book, you’ll be able to build a variety of deep learning XAI models and perform validation to assess their explainability.

What you will learn

• Explore deep learning frameworks for anomaly detection
• Mitigate bias to ensure unbiased and ethical analysis
• Increase your privacy and regulatory compliance awareness
• Build deep learning anomaly detectors in several domains
• Compare intrinsic and post hoc explainability methods
• Examine backpropagation and perturbation methods
• Conduct model-agnostic and model-specific explainability techniques
• Evaluate the explainability of your deep learning models