Titlebook: Beginning Anomaly Detection Using Python-Based Deep Learning; Implement Anomaly De Suman Kalyan Adari,Sridhar Alla Book 2024Latest edition

subordinate

Qian Feng,Dongjing Cao,Shulong Bao,Lu LiuIn this chapter, you will learn about anomalies in general, the categories of anomalies, and anomaly detection. You will also learn why anomaly detection is important, how anomalies can be detected, and the use case for such a mechanism.

祖先

https://doi.org/10.1007/978-981-19-5096-4This chapter introduces you to the isolation forest and the one-class support vector machine algorithms and walks you through how to use them for anomaly detection. In the process, you will also practice incorporating the fundamental machine learning workflow and incorporating hyperparameter tuning using the validation set.

Generator

效果

Xinru Liu,Mingtao Pei,Wei Liang,Zhengang NieIn this chapter, you will learn about generative adversarial networks as well as how you can implement anomaly detection using them.

BLAND

使絕緣

Jun Lin,Zhengyong Feng,Jialiang TangIn this chapter, you will learn about transformer networks and how you can implement anomaly detection using a transformer.

語(yǔ)源學(xué)

Introduction to Anomaly Detection,In this chapter, you will learn about anomalies in general, the categories of anomalies, and anomaly detection. You will also learn why anomaly detection is important, how anomalies can be detected, and the use case for such a mechanism.

細(xì)菌等

Gourmet

Autoencoders,In this chapter, you will learn about autoencoder neural networks and the different types of autoencoders. You will also learn how autoencoders can be used to detect anomalies and how you can implement anomaly detection using autoencoders.

TEM