Intro; Table of Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Introduction; Chapter 1: What Is Anomaly Detection?; What Is an Anomaly?; Anomalous Swans; Anomalies as Data Points; Anomalies in a Time Series; Taxi Cabs; Categories of Anomalies; Data Point-Based Anomalies; Context-Based Anomalies; Pattern-Based Anomalies; Anomaly Detection; Outlier Detection; Noise Removal; Novelty Detection; The Three Styles of Anomaly Detection; Where Is Anomaly Detection Used?; Data Breaches; Identity Theft; Manufacturing; Networking; Medicine; Video Surveillance; Summary
Chapter 2: Traditional Methods of Anomaly DetectionData Science Review; Isolation Forest; Mutant Fish; Anomaly Detection with Isolation Forest; One-Class Support Vector Machine; Anomaly Detection with OC-SVM; Summary; Chapter 3: Introduction to Deep Learning; What Is Deep Learning?; Artificial Neural Networks; Intro to Keras: A Simple Classifier Model; Intro to PyTorch: A Simple Classifier Model; Summary; Chapter 4: Autoencoders; What Are Autoencoders?; Simple Autoencoders; Sparse Autoencoders; Deep Autoencoders; Convolutional Autoencoders; Denoising Autoencoders; Variational Autoencoders
Chapter 7: Temporal Convolutional NetworksWhat Is a Temporal Convolutional Network?; Dilated Temporal Convolutional Network; Anomaly Detection with the Dilated TCN; Encoder-Decoder Temporal Convolutional Network; Anomaly Detection with the ED-TCN; Summary; Chapter 8: Practical Use Cases of Anomaly Detection; Anomaly Detection; Real-World Use Cases of Anomaly Detection; Telecom; Banking; Environmental; Healthcare; Transportation; Social Media; Finance and Insurance; Cybersecurity; Video Surveillance; Manufacturing; Smart Home; Retail; Implementation of Deep Learning-Based Anomaly Detection
