Adari Suman Kalyan - Beginning Anomaly detection using Python-based deep learning: with Keras and PyTorch

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-5176-8 . For more detailed information, please visit www.apress.com/source-code .

Congratulations on your decision to explore deep learning and the exciting world of anomaly detection using deep learning.

Anomaly detection is finding patterns that do not adhere to what is considered as normal or expected behavior. Businesses could lose millions of dollars due to abnormal events. Consumers could also lose millions of dollars. In fact, there are many situations every day where peoples lives are at risk and where their property is at risk. If your bank account gets cleaned out, that is a problem. If your water line breaks, flooding your basement, thats a problem. If all flights get delayed in the airport, causing long delays, thats a problem. You might have been misdiagnosed or not diagnosed at all with a health issue, which is a very big problem directly impacting your well-being.

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-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 an idea of the possibilities and concepts behind the thought process.

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 used for decades. Then we will look at deep learning to get a taste of it.

Then we will explore autoencoders and variational autoencoders, which are paving the way for the next generation of generative models.

We will explore RBM (Boltzmann machines) as way to detect anomalies. Then well look at LSTMs (long short-term memory) models to see how temporal data can be processed.

We will cover TCN (Temporal Convolutional Networks) , which are the best in class for temporal data anomaly detection. Finally, we will look at several examples of anomaly detection in various business use cases.

In addition, we will also cover Keras and PyTorch, the two most popular deep learning frameworks in detail in the Appendix chapters.

You will combine all this extensive knowledge with hands-on coding using Jupyter notebook-based exercises to experience the knowledge first hand and see where you can use these algorithms and frameworks.

Best of luck and welcome to the world of deep learning!

Sridhar Alla

I would like to thank my wonderful, loving wife, Rosie Sarkaria, and my beautiful, loving daughter, Evelyn, for all their love and patience during the many months I spent writing this book. I would also like to thank my parents, Ravi and Lakshmi Alla, for their blessings and all the support and encouragement they continue to bestow upon me.

Suman Kalyan Adari

I would like to thank my parents, Krishna and Jyothi, and my loving dog, Pinky, for supporting me throughout the entire process of writing my first book. I would especially like to thank my sister, Niha, for helping me with graph creation, proof-reading, editing, and testing the code samples.