Deep Learning and XAI Techniques for Anomaly Detection

Deep Learning and XAI Techniques for Anomaly Detection offers a thorough dive into explainable artificial intelligence (XAI) as it applies to anomaly detection. You will learn to build transparent models that balance performance with ethical considerations, ensuring both regulatory compliance and insightful analysis.

What this Book will help me do

• Understand the core principles of deep learning and anomaly detection to effectively apply them in various domains.
• Gain proficiency in utilizing Explainable AI methods to ensure model transparency and increase stakeholder trust.
• Implement techniques to mitigate bias in analytical models, fostering ethical and fair outcomes.
• Master the construction and evaluation of model-agnostic and model-specific explainability techniques.
• Develop strategies for assessing the explainability and reliability of deep learning models in practical applications.

Author(s)

Cher Simon, a seasoned expert specializing in deep learning and artificial intelligence, has a passion for sharing the practical applications of cutting-edge techniques. With years of experience in implementing XAI and anomaly detection models, Cher provides a practical perspective, blending theoretical insights with actionable solutions.

Who is it for?

This book is perfect for data scientists, machine learning practitioners, and technical leaders who aim to implement explainable AI solutions. It's ideal if you have a foundational knowledge of Python and deep learning. Whether you're developing models or leading projects, this book will elevate your skills.