Overview
- 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)
Keywords
About this book
This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics oftime series-based anomaly detection.
By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch.
What You Will Learn
- 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
Who This Book Is For
Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection
Authors and Affiliations
About the authors
Suman Kalyan Adari is an undergraduate student pursuing a BS degree in Computer Science at the University of Florida. He has been conducting deep learning research in the field of cybersecurity since his freshman year, and has presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon, USA in June 2019. He is quite passionate about deep learning, and specializes in its practical uses in various fields such as video processing, image recognition, anomaly detection, targeted adversarial attacks, and more.
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, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Sridhar Alla, Suman Kalyan Adari 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