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Beginning anomaly detection using Python-based deep learning : with Keras and PyTorch /
Chapter 5: Boltzmann Machines; What Is a Boltzmann Machine?; Restricted Boltzmann Machine (RBM); Anomaly Detection with the RBM - Credit Card Data Set; Anomaly Detection with the RBM - KDDCUP Data Set; Summary; Chapter 6: Long Short-Term Memory Models; Sequences and Time Series Analysis; What Is a R...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Apress,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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