Deep Learning and XAI Techniques for Anomaly Detection
Integrate the theory and practice of deep anomaly explainability
Seiten
2023
Packt Publishing Limited (Verlag)
978-1-80461-775-5 (ISBN)
Packt Publishing Limited (Verlag)
978-1-80461-775-5 (ISBN)
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.
Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guide
Purchase of the print or Kindle book includes a free PDF eBook
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
Book DescriptionDespite 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
Who this book is forThis 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.
Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guide
Purchase of the print or Kindle book includes a free PDF eBook
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
Book DescriptionDespite 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
Who this book is forThis 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.
Cher Simon is a principal solutions architect specializing in artificial intelligence, machine learning, and data analytics at AWS. Cher has 20 years of experience in architecting enterprise-scale, data-driven, and AI-powered industry solutions. Besides building cloud-native solutions in her day-to-day role with customers, Cher is also an avid writer and a frequent speaker at AWS conferences.
Table of Contents
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
| Erscheinungsdatum | 06.02.2023 |
|---|---|
| Vorwort | Jeff Barr |
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Software Entwicklung ► User Interfaces (HCI) | |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| ISBN-10 | 1-80461-775-X / 180461775X |
| ISBN-13 | 978-1-80461-775-5 / 9781804617755 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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