Beginning Anomaly Detection Using Python-Based Deep Learning
With Keras and PyTorch
1st Edition
Published on 11. October 2019
Book
Paperback/Softback
XVI, 416 pages
978-1-4842-5176-8 (ISBN)
Description
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.
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.
What You Will Learn
Who This Book Is For
Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection
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.
What You Will Learn
-
Understand what anomaly detection is and why it is important in today's world
-
Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn
-
Know the basics of deep learning in Python using Keras and PyTorch
-
Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more
-
Apply deep learning to semi-supervised and unsupervised anomaly detection
Who This Book Is For
Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection
More details
Edition
1st ed.
Language
English
Place of publication
CA
United States
Target group
Professional and scholarly
Illustrations
530
530 s/w Abbildungen
XVI, 416 p. 530 illus.
Dimensions
Height: 25.4 cm
Width: 17.8 cm
Weight
817 gr
ISBN-13
978-1-4842-5176-8 (9781484251768)
DOI
10.1007/978-1-4842-5177-5
Schweitzer Classification
Other editions
New editions
Suman Kalyan Adari | Sridhar Alla
Beginning Anomaly Detection Using Python-Based Deep Learning
Implement Anomaly Detection Applications with Keras and PyTorch
Book
01/2024
2nd Edition
APress
€58.84
Shipment within 10-15 days
Additional editions
Sridhar Alla | Suman Kalyan Adari
Beginning Anomaly Detection Using Python-Based Deep Learning
With Keras and PyTorch
E-Book
10/2019
APress
€56.99
Available for download
Persons
Sridhar Alla
is the co-founder and CTO of Bluewhale, which helps organizations big and small in building AI-driven big data solutions and analytics. He is a published author of books and an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. He also has several patents filed with the US PTO on large-scale computing and distributed systems. He has extensive hands-on experience in several technologies, including Spark, Flink, Hadoop, AWS, Azure, Tensorflow, Cassandra, and others. He spoke on Anomaly Detection Using Deep Learning at Strata SFO in March 2019 and will also present at Strata London in October 2019. He was born in Hyderabad, India and now lives in New Jersey, USA with his wife Rosie and daughter Evelyn. When he is not busy writing code, he loves to spend time with his family and also training, coaching, and organizing meetups.
Suman Kalyan Adari is an undergraduate student pursuing a BS degree in Computer Science at the University of Florida. He has been conducting deep learning research in the field of cybersecurity since his freshman year, and has presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon, USA in June 2019. He is quite passionate about deep learning, and specializes in its practical uses in various fields such as video processing, image recognition, anomaly detection, targeted adversarial attacks, and more.
Suman Kalyan Adari is an undergraduate student pursuing a BS degree in Computer Science at the University of Florida. He has been conducting deep learning research in the field of cybersecurity since his freshman year, and has presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon, USA in June 2019. He is quite passionate about deep learning, and specializes in its practical uses in various fields such as video processing, image recognition, anomaly detection, targeted adversarial attacks, and more.
Content
Chapter 1: What is Anomaly Detection?Chapter Goal: Introduce reader to the task of anomaly detection, where it's used, why it's important, as well as the different types of "anomaly detection" there are. No of pages: 30Sub -Topics1. What is an anomaly2. Use cases today3. Different types of anomalies
Chapter 2: Traditional Methods of Anomaly DetectionChapter Goal: Introduce reader to a couple high performing traditional methods of anomaly detection in Scikit-Learn. Evaluation metrics are performed on both (will be set as the benchmark of comparison for the deep learning models later on)No of pages: 50Sub - Topics1. Isolation Forest2. One class support vector machine3. Mahalanobis distance based anomaly detection
Chapter 3: Intro to Keras and PyTorchChapter Goal: Introduce reader to deep learning and how to build, train a basic model in both Keras and in PyTorch. Additionally, perform evaluation metrics on both. Also, discuss the various deep learning models that can be applied to semi-supervised and unsupervised anomaly detection.No of pages : 40Sub - Topics: 1.What is deep learning?2. Intro to Keras: simple classifier model3. Intro to PyTorch: simple classifier model4. How can we apply deep learning to anomaly detection?
Chapter 4: AutoencodersChapter Goal: Introduce reader to several autoencoders and how they can perform anomaly detection in both semi-supervised and unsupervised anomaly detection.No of pages: 40Sub - Topics: 1. What are autoencoders?2. Basic autoencoder3. Denoising autoencoder4. Variational autoencoder5. Summary of autoencoders as a model
Chapter 5: Boltzmann MachinesChapter Goal: Introduce reader to a restricted Boltzmann machine, deep Boltzmann machine, and a deep belief network.No of pages: 30Sub - Topics: 1. What is a Boltzmann machine?2. RBM3. DBM4. DBN5. Summary of the models
Chapter 6: Time-Series Anomaly DetectionChapter Goal: Introduce reader to RNNs and LSTMs for time series anomaly detection.No of pages:Sub - Topics: 301. What is a time series and how do we detect anomalies in that?2. What is an RNN3. RNN application4. What is an LSTM?5. LSTM application 6. Summary of the models
Chapter 7: Temporal Convolutional NetworkChapter Goal: Introduce reader to the TCN and how it can be used in anomaly detection.No of pages: 30Sub - Topics: 1. What is a TCN?2. Encoder-Decoder TCN3. Dilated TCN4. Summary of models
Chapter 8: Practical Use Cases of Anomaly DetectionChapter Goal: Illustrate common use cases.No of pages: 30Sub - Topics: 1. Use cases
Appendix A: Introduction to KerasChapter Goal: Introduce reader to the Keras No of pages: 30Sub - Topics: 1. What is a Keras?2. How to use it
Appendix B: Introduction to PyTorchChapter Goal: Introduce reader to the PyTorchNo of pages: 30Sub - Topics: 1. What is a PyTorch?2. How to use it
Chapter 2: Traditional Methods of Anomaly DetectionChapter Goal: Introduce reader to a couple high performing traditional methods of anomaly detection in Scikit-Learn. Evaluation metrics are performed on both (will be set as the benchmark of comparison for the deep learning models later on)No of pages: 50Sub - Topics1. Isolation Forest2. One class support vector machine3. Mahalanobis distance based anomaly detection
Chapter 3: Intro to Keras and PyTorchChapter Goal: Introduce reader to deep learning and how to build, train a basic model in both Keras and in PyTorch. Additionally, perform evaluation metrics on both. Also, discuss the various deep learning models that can be applied to semi-supervised and unsupervised anomaly detection.No of pages : 40Sub - Topics: 1.What is deep learning?2. Intro to Keras: simple classifier model3. Intro to PyTorch: simple classifier model4. How can we apply deep learning to anomaly detection?
Chapter 4: AutoencodersChapter Goal: Introduce reader to several autoencoders and how they can perform anomaly detection in both semi-supervised and unsupervised anomaly detection.No of pages: 40Sub - Topics: 1. What are autoencoders?2. Basic autoencoder3. Denoising autoencoder4. Variational autoencoder5. Summary of autoencoders as a model
Chapter 5: Boltzmann MachinesChapter Goal: Introduce reader to a restricted Boltzmann machine, deep Boltzmann machine, and a deep belief network.No of pages: 30Sub - Topics: 1. What is a Boltzmann machine?2. RBM3. DBM4. DBN5. Summary of the models
Chapter 6: Time-Series Anomaly DetectionChapter Goal: Introduce reader to RNNs and LSTMs for time series anomaly detection.No of pages:Sub - Topics: 301. What is a time series and how do we detect anomalies in that?2. What is an RNN3. RNN application4. What is an LSTM?5. LSTM application 6. Summary of the models
Chapter 7: Temporal Convolutional NetworkChapter Goal: Introduce reader to the TCN and how it can be used in anomaly detection.No of pages: 30Sub - Topics: 1. What is a TCN?2. Encoder-Decoder TCN3. Dilated TCN4. Summary of models
Chapter 8: Practical Use Cases of Anomaly DetectionChapter Goal: Illustrate common use cases.No of pages: 30Sub - Topics: 1. Use cases
Appendix A: Introduction to KerasChapter Goal: Introduce reader to the Keras No of pages: 30Sub - Topics: 1. What is a Keras?2. How to use it
Appendix B: Introduction to PyTorchChapter Goal: Introduce reader to the PyTorchNo of pages: 30Sub - Topics: 1. What is a PyTorch?2. How to use it