Deep Learning and XAI Techniques for Anomaly Detection
Deep Learning and XAI Techniques for Anomaly Detection shows you how to evaluate and create explainable models, leading to increased interpretability and trust in model predictions with better performance. You’ll explore the fundamentals of deep learning, anomaly detection, and XAI using practical examples and self-assessment questions.
Offered by
Difficulty Level
Intermediate
Completion Time
7h16m
Language
English
About Book
Who Is This Book For?
This book is for anyone who aspires to explore explainable deep learning anomaly detection, tenured data scientists or ML practitioners looking for Explainable AI (XAI) best practices, or business leaders looking to make decisions on trade-off between performance and interpretability of anomaly detection applications. A basic understanding of deep learning and anomaly detection–related topics using Python is recommended to get the most out of this book.
Deep Learning and XAI Techniques for Anomaly Detection
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 7h16m total length
Understanding Deep Learning Anomaly Detection
Understanding Explainable AI
Natural Language Processing Anomaly Explainability
Time Series Anomaly Explainability
Computer Vision Anomaly Explainability
Differentiating Intrinsic versus Post Hoc Explainability
Backpropagation Versus Perturbation Explainability
Model-Agnostic versus Model-Specific Explainability
Explainability Evaluation Schemes
Related Resources
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