Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch

✍ Scribed by Suman Kalyan Adari, Sridhar Alla

• Publisher: Apress
• Year: 2024
• Tongue: English
• Leaves: 538
• Edition: 2
• Category: Library

✦ Synopsis

This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning.

 

Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering  transformer architecture in the context of time-series anomaly detection. 

 

After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors.

 

What You Will Learn

• Understand what anomaly detection is, why it it is important, and how it is applied
• Grasp the core concepts of machine learning.
• Master traditional machine learning approaches to anomaly detection using scikit-kearn.
• Understand deep learning in Python using Keras and PyTorch
• Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall
• Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications

 

Who This Book Is For

Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.

✦ Table of Contents

Table of Contents
About the Authors
About the Technical Reviewers
Acknowledgments
Introduction
Chapter 1: Introduction to Anomaly Detection
What Is an Anomaly?
Anomalous Swans
Anomalies as Data Points
Anomalies in a Time Series
Personal Spending Pattern
Taxi Cabs
Categories of Anomalies
Data Point–Based Anomalies
Context-Based Anomalies
Pattern-Based Anomalies
Anomaly Detection
Outlier Detection
Noise Removal
Novelty Detection
Event Detection
Change Point Detection
Anomaly Score Calculation
The Three Styles of Anomaly Detection
Where Is Anomaly Detection Used?
Data Breaches
Identity Theft
Manufacturing
Networking
Medicine
Video Surveillance
Environment
Summary
Chapter 2: Introduction to Data Science
Data Science
Dataset
Pandas, Scikit-Learn, and Matplotlib
Data I/O
Data Loading
Data Saving
DataFrame Creation
Data Manipulation
Select
Filtering
Sorting
Applying Functions
Grouping
Combining DataFrames
Creating, Renaming, and Dropping Columns
Data Analysis
Value Counts
Pandas .describe() Method
Pandas Correlation Matrix
Visualization
Line Chart
Chart Customization
Scatter Plot
Histogram
Bar Graph
Data Processing
Nulls
Categorical Encoding
Scaling and Normalizing
Feature Engineering and Selection
Summary
Chapter 3: Introduction to Machine Learning
Machine Learning
Introduction to Machine Learning
Data Splitting
Modeling and Evaluation
Classification Metrics
Regression Metrics
Overfitting and Bias-Variance Tradeoff
Hyperparameter Tuning
Validation
Summary
Chapter 4: Traditional Machine Learning Algorithms
Traditional Machine Learning Algorithms
Isolation Forest
Example of an Isolation Forest
Anomaly Detection with an Isolation Forest
Data Preparation
Training
Hyperparameter Tuning
Evaluation and Summary
One-Class Support Vector Machine
How Does OC-SVM Work?
Anomaly Detection with OC-SVM
Data Preparation
Training
Hyperparameter Tuning
Evaluation and Summary
Summary
Chapter 5: Introduction to Deep Learning
Introduction to Deep Learning
What Is Deep Learning?
The Neuron
Activation Functions
Neural Networks
Loss Functions
Regression
Classification
Gradient Descent and Backpropagation
Loss Curve
Regularization
Optimizers
Multilayer Perceptron Supervised Anomaly Detection
Simple Neural Network: Keras
Simple Neural Network: PyTorch
Summary
Chapter 6: Autoencoders
What Are Autoencoders?
Simple Autoencoders
Sparse Autoencoders
Deep Autoencoders
Convolutional Autoencoders
Denoising Autoencoders
Variational Autoencoders
Summary
Chapter 7: Generative Adversarial Networks
What Is a Generative Adversarial Network?
Generative Adversarial Network Architecture
Wasserstein GAN
WGAN-GP
Anomaly Detection with a GAN
Summary
Chapter 8: Long Short-Term Memory Models
Sequences and Time Series Analysis
What Is an RNN?
What Is an LSTM?
LSTM for Anomaly Detection
Examples of Time Series
art_daily_no_noise.csv
art_daily_nojump.csv
art_daily_jumpsdown.csv
art_daily_perfect_square_wave.csv
art_load_balancer_spikes.csv
ambient_temperature_system_failure.csv
ec2_cpu_utilization.csv
rds_cpu_utilization.csv
Summary
Chapter 9: Temporal Convolutional Networks
What Is a Temporal Convolutional Network?
Dilated Temporal Convolutional Network
Anomaly Detection with the Dilated TCN
Encoder-Decoder Temporal Convolutional Network
Anomaly Detection with the ED-TCN
Summary
Chapter 10: Transformers
What Is a Transformer?
Transformer Architecture
Transformer Encoder
Transformer Decoder
Transformer Inference
Anomaly Detection with the Transformer
Summary
Chapter 11: Practical Use Cases and Future Trends of Anomaly Detection
Anomaly Detection
Real-World Use Cases of Anomaly Detection
Telecom
Banking
Environmental
Health Care
Transportation
Social Media
Finance and Insurance
Cybersecurity
Video Surveillance
Manufacturing
Smart Home
Retail
Implementation of Deep Learning–Based Anomaly Detection
Future Trends
Summary
Index