Titlebook: Beginning Anomaly Detection Using Python-Based Deep Learning; With Keras and PyTor Sridhar‘Alla,Suman Kalyan Adari Book 20191st edition Sri

Cerebrovascular

地牢

煩人

aggrieve

Traditional Methods of Anomaly Detection,In this chapter, you will learn about traditional methods of anomaly detection. You will also learn how various statistical methods and machine learning algorithms work and how they can be used to detect anomalies and how you can implement anomaly detection using several algorithms.

合法

潰爛

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.

預(yù)定

蓋他為秘密

Temporal Convolutional Networks,In this chapter, you will learn about temporal convolutional networks (TCNs). You will also learn how TCNs work and how they can be used to detect anomalies and how you can implement anomaly detection using a TCN.

Exclude

Book 20191st editionin 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 l

Palate

Covers the most contemporary approaches to anomaly detectionUtilize 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-sup