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

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 Description

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

Who this book is 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.

創建可解釋的人工智慧模型，以透明且可解釋的方式進行異常檢測，本實用指南將協助您達成此目標。

購買紙本書或Kindle電子書，將獲得免費PDF電子書。

主要特點：

- 建立可稽核的XAI模型，以確保可複製性和法規合規性。
- 從透明的異常檢測模型中獲取關鍵洞察。
- 在模型準確性和可解釋性之間取得適當平衡。

書籍描述：

儘管有許多有希望的進展，深度學習模型的不透明性使其難以解釋，這在實際應用和法規合規方面是一個缺點。

《深度學習和XAI技術用於異常檢測》向您展示了最先進的方法，幫助您理解和應對這些挑戰。通過利用本書中描述的可解釋人工智慧（XAI）和深度學習技術，您將學會如何在確保公平和道德分析的同時成功提取業務關鍵洞察。

本實用指南將為您提供工具和最佳實踐，以實現深度學習模型的透明性和可解釋性，從而在您的異常檢測應用中建立信任。在各章中，您將獲得XAI和異常檢測知識，使您能夠開展一系列真實世界的項目。無論您是在建立計算機視覺、自然語言處理還是時間序列模型，您都將學習如何量化和評估其可解釋性。

通過閱讀本深度學習書籍，您將能夠建立各種深度學習XAI模型並進行驗證，以評估其可解釋性。

學到的內容：

- 探索用於異常檢測的深度學習框架。
- 減輕偏見，確保無偏和道德分析。
- 提高隱私和法規合規意識。
- 在多個領域中建立深度學習異常檢測器。
- 比較內在和事後解釋性方法。
- 檢查反向傳播和擾動方法。
- 進行模型無關和模型特定的解釋技術。
- 評估您的深度學習模型的可解釋性。

適合閱讀對象：

本書適合任何希望探索可解釋的深度學習異常檢測的人，包括有經驗的數據科學家或機器學習從業者尋找可解釋人工智慧（XAI）的最佳實踐，以及希望在性能和可解釋性之間做出決策的業務領導者。建議具備基本的深度學習和異常檢測相關主題的Python知識，以充分利用本書。

1. Understanding Deep Learning Anomaly Detection
2. Understanding Explainable AI
3. Natural Language Processing Anomaly Explainability
4. Time Series Anomaly Explainability
5. Computer Vision Anomaly Explainability
6. Differentiating Intrinsic versus Post Hoc Explainability
7. Backpropagation Versus Perturbation Explainability
8. Model-Agnostic versus Model-Specific Explainability
9. Explainability Evaluation Schemes

1. 深度學習異常檢測的理解
2. 可解釋人工智慧的理解
3. 自然語言處理異常解釋能力
4. 時間序列異常解釋能力
5. 電腦視覺異常解釋能力
6. 內在解釋能力與事後解釋能力的區別
7. 反向傳播與擾動解釋能力的區別
8. 模型無關與模型特定的解釋能力
9. 解釋能力評估方案