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Beginning Anomaly Detection Using Python

Publisher: Apress
Author: Sridhar Alla, Suman Kalyan Adari
ISBN-13: 978-1484251768
ISBN-10: 1484251768
Pages: 432
Language: English
Year: 2019
File: ebook PDF

With Keras and PyTorch.

Book DescriptionTable of contentslink

Book Description

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. 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. Beginning Anomaly Detection Using Python

About the authors

Sridhar Alla — He is a published author of books and an active speaker at numerous conferences Strata, Hadoop World, Spark Summit and more. He also holds several US PTO patents for large scale computing and distributed systems.

Suman Kalyan Adari is an undergraduate student pursuing a bachelor’s degree in computer science from the University of Florida. He specializes in deep learning in cybersecurity.

Table of contents

Chapter 1: What Is Anomaly Detection?
Chapter 2: Traditional Methods of Anomaly Detection
Chapter 3: Introduction to Deep Learning
Chapter 4: Autoencoders
Chapter 5: Boltzmann Machines
Chapter 6: Long Short-Term Memory Models
Chapter 7: Temporal Convolutional Networks
Chapter 8: Practical Use Cases of Anomaly Detection
Appendix A: Intro to Keras
Appendix B: Intro to PyTorch
Index

Beginning Anomaly Detection Using Python

Live Object Detection in Python

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