Beginning Anomaly Detection Using Python-Based Deep Learning, Adari S.K., Alla S., 2024

Beginning Anomaly Detection Using Python-Based Deep Learning, Adari S.K., Alla S., 2024.
     
   In this book, you will learn how anomaly detection can be used to solve business problems. You will explore how anomaly detection techniques can be used to address practical use cases and address real-life problems in the business landscape. Every business and use case is different, so while we cannot copy and paste code and build a successful model to detect anomalies in any dataset, this book will cover many use cases with hands-on coding exercises to give you an idea of the possibilities and concepts behind the thought process. All the code examples in the book are presented in Python 3-8. We choose Python because it is truly the best language for data science, with a plethora of packages and integrations with scikit-learn, deep learning libraries, etc. We will start by introducing anomaly detection, and then we will look at legacy methods of detecting anomalies that have been used for decades.




Pattern-Based Anomalies.
Pattern-based anomalies are patterns and trends that deviate from their historical counterparts, and they often occur in time-series or other sequence-based data. In the earlier taxi cab company example, the customer pickup counts for the month of April were pretty consistent with the rest of the year. However, once the polar vortex hit, the numbers tanked visibly, resulting in a huge drop in the graph, labeled as an anomaly.

Similarly, when monitoring network traffic in the workplace, expected patterns of network traffic are formed from constant monitoring of data over several months or even years for some companies. If an employee attempts to download or upload large volumes of data, it generates a certain pattern in the overall network traffic flow that could be considered anomalous if it deviates from the employee’s usual behavior.

CONTENTS.
Table of Contents.
About the Authors.
About the Technical Reviewers.
Acknowledgments.
Introduction.
Chapter 1: Introduction to Anomaly Detection.
Chapter 2: Introduction to Data Science.
Chapter 3: Introduction to Machine Learning.
Chapter 4: Traditional Machine Learning Algorithms.
Chapter 5: Introduction to Deep Learning.
Chapter 6: Autoencoders.
Chapter 7: Generative Adversarial Networks.
Chapter 8: Long Short-Term Memory Models.
Chapter 9: Temporal Convolutional Networks.
Chapter 10: Transformers.
Chapter 11: Practical Use Cases and Future Trends of Anomaly Detection.
Index.



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