Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and Pytorch

ISBN-13: 9781484251768 / Angielski / Miękka / 2019 / 416 str.

Sridhar Alla; Suman Adari

Opis

Beg-Int user level

Spis treści

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: 30

Sub -Topics

1. What is an anomaly

2. Use cases today

3. Different types of anomalies

Chapter 2:  Traditional Methods of Anomaly Detection

Chapter 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: 50

Sub - Topics

1.   Isolation Forest

2.   One class support vector machine

3.   Mahalanobis distance based anomaly detection

  

Chapter 3: Intro to Keras and PyTorch

Chapter 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 : 40

Sub - Topics:  

1.What is deep learning?

2. Intro to Keras: simple classifier model

3. Intro to PyTorch: simple classifier model

4. How can we apply deep learning to anomaly detection? 

Chapter 4: Autoencoders

Chapter Goal: Introduce reader to several autoencoders and how they can perform anomaly detection in both semi-supervised and unsupervised anomaly detection.

No of pages: 40

Sub - Topics: 

1. What are autoencoders?

2. Basic autoencoder

3. Denoising autoencoder

4. Variational autoencoder

5. Summary of autoencoders as a model

Chapter 5: Boltzmann Machines

Chapter Goal: Introduce reader to a restricted Boltzmann machine, deep Boltzmann machine, and a deep belief network.

No of pages: 30

Sub - Topics: 

1. What is a Boltzmann machine?

2. RBM

3. DBM

4. DBN

5. Summary of the models

Chapter 6: Time-Series Anomaly Detection

Chapter Goal: Introduce reader to RNNs and LSTMs for time series anomaly detection.

No of pages:

Sub - Topics: 30

1. What is a time series and how do we detect anomalies in that?

2. What is an RNN

3. RNN application

4. What is an LSTM?

5. LSTM application 

6. Summary of the models

Chapter 7: Temporal Convolutional Network

Chapter Goal: Introduce reader to the TCN and how it can be used in anomaly detection.

No of pages: 30

Sub - Topics: 

1. What is a TCN?

2. Encoder-Decoder TCN

3. Dilated TCN

4. Summary of models

Chapter 8: Practical Use Cases of Anomaly Detection

Chapter Goal: Illustrate common use cases.

No of pages: 30

Sub - Topics: 

1. Use cases

Appendix A: Introduction to Keras

Chapter Goal: Introduce reader to the Keras 

No of pages: 30

Sub - Topics: 

1. What is a Keras?

2. How to use it

Appendix B: Introduction to PyTorch

Chapter Goal: Introduce reader to the PyTorch

No of pages: 30

Sub - Topics: 

1. What is a PyTorch?

2. How to use it

O autorze

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. 

Blurb

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'll 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