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This is the code repository for Deep Learning and XAI Techniques for Anomaly Detection , published by Packt.
Integrate the theory and practice of deep anomaly explainability
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
df = pd.read_csv('export_food.csv')
data = df[['reviews','ratings']]
data['reviews'] = data['reviews'].str.strip()
data.head(3)
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the 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 required | OS required |
|---|---|
| Python 3.8+ | Windows, Mac OS X, and Linux (Any) |
| TensorFlow 2.11+ | Windows, Mac OS X, and Linux (Any) |
| AutoGluon 0.6.1+ | Windows, Mac OS X, and Linux (Any) |
| Cleanlab 2.2.0+ | Windows, Mac OS X, and Linux (Any) |
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).
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.