Beginning Anomaly Detection Using Python-Based Deep Learning, 2nd Edition Suman Kalyan Adari

Beginning Anomaly Detection Using Python-Based Deep Learning, 2nd Edition – Ebook Instant Download/Delivery ISBN(s): 9798868800078,8868800071

Product details:

• ISBN-10 ‏ : ‎ 1484251768
• ISBN-13 ‏ : ‎ 978-1484251768
• Author: Sridhar Alla

Utilize this easy-to-follow beginner’s guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks.
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 of time series-based anomaly detection.

Table contents:

1. What Is Anomaly Detection?
2. Traditional Methods of Anomaly Detection
3. Introduction to Deep Learning
4. Autoencoders
5. Boltzmann Machines
6. Long Short-Term Memory Models
7. Temporal Convolutional Networks
8. Practical Use Cases of Anomaly Detection

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