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 Description
«Beginning Anomaly Detection Using Python» by Sridhar Alla is a comprehensive guide for beginners who want to learn about anomaly detection techniques using Python. The book is designed to provide a step-by-step introduction to the concepts of anomaly detection, data preprocessing, feature engineering, model building, and evaluation.
The author has done an excellent job of explaining complex concepts in a clear and concise manner. Each chapter of the book covers a specific topic, and the chapters are organized in a logical sequence. The book starts with an introduction to anomaly detection, followed by a discussion on data preprocessing techniques. The author then discusses various feature engineering techniques, such as feature scaling, feature selection, and dimensionality reduction.
One of the strengths of this book is its coverage of various machine learning algorithms used for anomaly detection, including k-NN, SVM, and Random Forest. The author explains the pros and cons of each algorithm and provides detailed instructions on how to implement them in Python.
The book also covers some advanced topics, such as time-series anomaly detection and unsupervised anomaly detection using clustering algorithms. The author provides a detailed explanation of these topics, making it easy for readers to understand the concepts and implement them in their projects.
The book includes several examples and case studies, which help readers to understand the practical applications of anomaly detection. The author also provides sample code, datasets, and Jupyter notebooks, making it easy for readers to follow along with the examples.
Overall, «Beginning Anomaly Detection Using Python» is an excellent book for beginners who want to learn about anomaly detection techniques using Python. The book is well-written, easy to follow, and provides a comprehensive introduction to the topic. I highly recommend this book to anyone who is interested in learning about anomaly detection.
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
What Is Anomaly Detection?
Traditional Methods of Anomaly Detection
Introduction to Deep Learning
Autoencoders
Boltzmann Machines
Long Short-Term Memory Models
Temporal Convolutional Networks
Practical Use Cases of Anomaly Detection
Appendix A: Intro to Keras
Appendix B: Intro to PyTorch
Index
Beginning Anomaly Detection Using Python