Authors:
Explains some of the most effective and efficient anomaly detection methods available
Provides annotated Python code snippets and notebooks
Covers the most contemporary approaches to anomaly detection
Uses two popular deep learning frameworks—Keras and PyTorch
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Table of contents (8 chapters)
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Front Matter
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Back Matter
About this book
- Understand what anomaly detection is and why it is important in today's world
- Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn
- Know the basics of deep learning in Python using Keras and PyTorch
- Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more
- Apply deep learning to semi-supervised and unsupervised anomaly detection
Keywords
- Anamoly Detection
- Deep Learning
- Python
- Keras
- PyTorch
- Novelty detection
- Auto Encoders
- Fraud Detection
- Semi-supervised
- Unsupervised
Authors and Affiliations
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New Jersey, USA
Sridhar Alla
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Tampa, USA
Suman Kalyan Adari
About the authors
Bibliographic Information
Book Title: Beginning Anomaly Detection Using Python-Based Deep Learning
Book Subtitle: With Keras and PyTorch
Authors: Sridhar Alla, Suman Kalyan Adari
DOI: https://doi.org/10.1007/978-1-4842-5177-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Professional and Applied Computing (R0), Apress Access Books
Copyright Information: Sridhar Alla, Suman Kalyan Adari 2019
Softcover ISBN: 978-1-4842-5176-8Published: 11 October 2019
eBook ISBN: 978-1-4842-5177-5Published: 10 October 2019
Edition Number: 1
Number of Pages: XVI, 416
Number of Illustrations: 530 b/w illustrations
Topics: Artificial Intelligence, Python, Open Source