You're reading from Deep Learning and XAI Techniques for Anomaly Detection

Product type Book

Published in Jan 2023

Publisher Packt

ISBN-13 9781804617755

Pages 218 pages

Edition 1st Edition

Languages

Concepts

Data Science

Author (1):

Cher Simon

Table of Contents (15) Chapters

Preface

1. Part 1 – Introduction to Explainable Deep Learning Anomaly Detection

2. Chapter 1: Understanding Deep Learning Anomaly Detection

3. Chapter 2: Understanding Explainable AI

4. Part 2 – Building an Explainable Deep Learning Anomaly Detector

5. Chapter 3: Natural Language Processing Anomaly Explainability

6. Chapter 4: Time Series Anomaly Explainability

7. Chapter 5: Computer Vision Anomaly Explainability

8. Part 3 – Evaluating an Explainable Deep Learning Anomaly Detector

9. Chapter 6: Differentiating Intrinsic and Post Hoc Explainability

10. Chapter 7: Backpropagation versus Perturbation Explainability

11. Chapter 8: Model-Agnostic versus Model-Specific Explainability

12. Chapter 9: Explainability Evaluation Schemes

13. Index

Why subscribe?

14. Other Books You May Enjoy

To get the most out of this book

You will need a Jupyter environment with Python 3.8+ to run the example walk-throughs in this book. Each sample notebook comes with a requirement.txt file that lists the package dependencies. You can experiment with the sample notebooks on Amazon SageMaker Studio Lab (https://aws.amazon.com/sagemaker/studio-lab/). This free ML development environment provides up to 12 hours of CPU or 4 hours of GPU per user session and 15 GiB storage at no cost.

Software/hardware covered in the book	Operating system requirements
Python 3.8+	Windows, macOS, or Linux
TensorFlow 2.11+	Windows, macOS, or Linux
AutoGluon 0.6.1+	Windows, macOS, or Linux
Cleanlab 2.2.0+	Windows, macOS, or Linux

A valid email address is all you need to get started with Amazon SageMaker Studio Lab. You do not need to configure infrastructure, manage identity and access, or even sign up for an AWS account. For more information, please refer to https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-overview.html. Alternatively, you can try the practical examples on your preferred Integrated Development Environment (IDE).

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

A basic understanding of deep learning and anomaly detection-related topics using Python is recommended. Each chapter comes with example walk-throughs that help you gain hands-on experience, except for Chapters 2 and 9, which focus more on conceptual discussions. We suggest running the provided sample notebooks while reading a specific chapter. Additional exercises are available in Chapters 3, 4, and 5 to reinforce your learning.