Deep learning and XAI techniques for anomaly detection : integrating the theory and practice of deep anomaly explainability

Deep learning and XAI techniques for anomaly detection integrating the theory and practice of deep anomaly explainability

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 practi...

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Detalles Bibliográficos
Otros Autores: Simon, Cher, author (author), Barr, Jeff, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing, Limited [2023]
Edición:1st ed
Materias:
Machine learning > Industrial applications.
Anomaly detection (Computer security)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009720309106719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Foreword
  • Contributors
  • Table of Contents
  • Preface
  • Part 1 - Introduction to Explainable Deep Learning Anomaly Detection
  • Chapter 1: Understanding Deep Learning Anomaly Detection
  • Technical Requirements
  • Exploring types of anomalies
  • Discovering real-world use cases
  • Detecting fraud
  • Predicting industrial maintenance
  • Diagnosing medical conditions
  • Monitoring cybersecurity threats
  • Reducing environmental impact
  • Recommending financial strategies
  • Considering when to use deep learning and what for
  • Understanding challenges and opportunities
  • Summary
  • Chapter 2: Understanding Explainable AI
  • Understanding the basics of XAI
  • Differentiating explainability versus interpretability
  • Contextualizing stakeholder needs
  • Implementing XAI
  • Reviewing XAI significance
  • Considering the right to explanation
  • Driving inclusion with XAI
  • Mitigating business risks
  • Choosing XAI techniques
  • Summary
  • Part 2 - Building an Explainable Deep Learning Anomaly Detector
  • Chapter 3: Natural Language Processing Anomaly Explainability
  • Technical requirements
  • Understanding natural language processing
  • Reviewing AutoGluon
  • Problem
  • Solution walk-through
  • Exercise
  • Chapter 4: Time Series Anomaly Explainability
  • Understanding time series
  • Understanding explainable deep anomaly detection for time series
  • Technical requirements
  • The problem
  • Solution walkthrough
  • Exercise
  • Summary
  • Chapter 5: Computer Vision Anomaly Explainability
  • Reviewing visual anomaly detection
  • Reviewing image-level visual anomaly detection
  • Reviewing pixel-level visual anomaly detection
  • Integrating deep visual anomaly detection with XAI
  • Technical requirements
  • Problem
  • Solution walkthrough
  • Exercise
  • Summary.
  • Part 3 - Evaluating an Explainable Deep Learning Anomaly Detector
  • Chapter 6: Differentiating Intrinsic and Post hoc Explainability
  • Technical requirements
  • Understanding intrinsic explainability
  • Intrinsic global explainability
  • Intrinsic local explainability
  • Understanding post hoc explainability
  • Post hoc global explainability
  • Post hoc local explainability
  • Considering intrinsic versus post hoc explainability
  • Summary
  • Chapter 7: Backpropagation versus Perturbation Explainability
  • Reviewing backpropagation explainability
  • Saliency maps
  • Reviewing perturbation explainability
  • LIME
  • Comparing backpropagation and perturbation XAI
  • Summary
  • Chapter 8: Model-Agnostic versus Model-Specific Explainability
  • Chapter 9: Explainability Evaluation Schemes
  • Reviewing the System Causability Scale (SCS)
  • Exploring Benchmarking Attribution Methods (BAM)
  • Understanding faithfulness and monotonicity
  • Human-grounded evaluation framework
  • Summary
  • Index
  • Other Books You May Enjoy.

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