用户名  找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

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

[复制链接]
楼主: Jefferson
发表于 2025-3-25 04:38:13 | 显示全部楼层
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.
发表于 2025-3-25 09:38:43 | 显示全部楼层
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.
发表于 2025-3-25 15:09:57 | 显示全部楼层
发表于 2025-3-25 15:49:15 | 显示全部楼层
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.
发表于 2025-3-25 22:26:34 | 显示全部楼层
发表于 2025-3-26 01:59:50 | 显示全部楼层
Jun Lin,Zhengyong Feng,Jialiang TangIn this chapter, you will learn about transformer networks and how you can implement anomaly detection using a transformer.
发表于 2025-3-26 04:55:23 | 显示全部楼层
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.
发表于 2025-3-26 11:06:08 | 显示全部楼层
发表于 2025-3-26 12:49:27 | 显示全部楼层
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.
发表于 2025-3-26 17:50:34 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-7-8 15:21
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表