Anomaly Detection Using a Variational Autoencoder Neural Network With a Novel Objective Function and Gaussian Mixture Model Selection Technique
Written by Brandon Bowman and published by the Naval Postgraduate School, this groundbreaking book presents an innovative approach to anomaly detection using variational autoencoders. The methodology it outlines is essential for researchers and professionals who are looking to deepen their understanding of machine learning and its applications in identifying anomalies effectively. This book is a must-read as it merges theory with practical techniques, ensuring you’re equipped with the advanced tools needed in today’s data-driven world.
Practical Machine Learning: A New Look at Anomaly Detection
This collaborative effort by Ted Dunning and Ellen Friedman offers a comprehensive guide on practical machine learning techniques for anomaly detection. The authors delve into various models and scenarios that highlight the relevance of machine learning in real-world applications. Whether you’re looking to detect fraud, monitor systems, or analyze data, this book serves as an invaluable resource, simplifying complex concepts into actionable insights that can be used immediately.
Machine Learning for Time-Series with Python
Ben Auffarth’s book on machine learning for time-series analysis offers an in-depth exploration of forecasting, predicting, and detecting anomalies. This text is particularly valuable for data scientists and analysts who specialize in time-series data since it employs state-of-the-art machine learning methods. The practical examples presented help demystify the intricate world of time-series data, making it accessible and beneficial for both novice and experienced practitioners.
Anomaly Detection and Complex Event Processing Over IoT Data Streams
Co-authored by Patrick Schneider and Fatos Xhafa, this book focuses on real-time data processing, offering strategies for monitoring IoT data streams, especially in the context of eHealth applications. This publication stands out due to its focus on complex event processing, making it particularly relevant for those looking to integrate IoT solutions into their anomaly detection systems. It’s a must-read for professionals aiming to leverage IoT for health monitoring and beyond.
Anomaly Detection With Time Series Forecasting
Hoan Dao’s insightful work highlights the intersection of time series forecasting and anomaly detection, making it an essential read for those focused on predictive modeling techniques. This book introduces readers to innovative forecasting methods that enhance the detection of outliers, providing invaluable tools for data analysis across various domains from finance to public health. The clear explanations and practical examples make this book accessible for both beginners and experts alike.
Anomaly Detection: Through Machine Learning, Deep Learning and AutoML
AM Govind Kumar provides a comprehensive overview of anomaly detection methodologies in this pivotal book. It combines machine learning, deep learning, and AutoML techniques that underpin today’s data analytics landscape. As data expands exponentially, this text becomes vital in equipping practitioners with powerful tools and methodologies to stay ahead in this rapidly evolving space.
The State of the Art in Intrusion Prevention and Detection
Al-Sakib Khan Pathan explores the latest advancements in intrusion prevention and detection systems within this essential compilation. This book addresses vital aspects of cybersecurity and anomaly detection, making it essential for professionals working in tech security. Dive into analyses that provide theoretical grounding and practical insights, critical for anyone looking to bolster their security measures against emerging threats.
Fraud and Fraud Detection, + Website: A Data Analytics Approach
Sunder Gee, in this compelling book, presents a thorough analysis of fraud detection techniques through the lens of data analytics. This resource provides readers with pivotal insights into identifying fraudulent activities across various sectors, thereby enhancing their understanding of preventive measures. Its practical focus and comprehensive approach make it a crucial addition to anyone’s collection who is engaged in risk management and fraud detection.
Deep Learning for Time Series Cookbook
Vitor Cerqueira and Luís Roque’s cookbook is a fantastic resource for practitioners looking to apply deep learning techniques to time series data effectively. With hands-on Python recipes for forecasting and anomaly detection, this book enables readers to create custom solutions while enhancing their data science skills. It’s a treasure trove of practical tips and methodologies for anyone interested in the cutting-edge field of machine learning.
Machine Learning approaches for Anomaly detection in Stock Securities
Pallavi Khadse’s book introduces innovative machine learning techniques specifically aimed at detecting anomalies in stock securities. The text is essential for financial analysts and traders who must continuously monitor stock performance for irregularities. The methodologies presented are both practical and advanced, ensuring that readers can implement them in real-time trading scenarios, making this book a critical tool in the financial landscape.
