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: Machine Learning for Computer Scientists and Data Analysts; From an Applied Pers Setareh Rafatirad,Houman Homayoun,Sai Manoj Puduko Textboo

[复制链接]
楼主: Optician
发表于 2025-3-25 05:04:06 | 显示全部楼层
Adversarial Machine Learningints are experimentally provided, such as up to 97.65% accuracy even against CW attack. Though adversarial learning’s effectiveness is enhanced, still it is shown in this work that it can be further exploited for vulnerabilities.
发表于 2025-3-25 11:26:49 | 显示全部楼层
发表于 2025-3-25 12:16:54 | 显示全部楼层
Transfer Learning in Mobile Healthting, and ends with a model generation module to carry out the machine learning task in the new platform or with the new user. The chapter uses . to realize the TransFall framework using time-series data.
发表于 2025-3-25 18:37:57 | 显示全部楼层
Textbook 2022neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve. In
发表于 2025-3-25 21:51:59 | 显示全部楼层
data being handled.Includes numerous, practical case-studies.This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution ne
发表于 2025-3-26 00:58:12 | 显示全部楼层
Setareh Rafatirad,Houman Homayoun,Sai Manoj PudukoDescribes traditional as well as advanced machine learning algorithms.Enables students to learn which algorithm is most appropriate for the data being handled.Includes numerous, practical case-studies
发表于 2025-3-26 05:56:24 | 显示全部楼层
http://image.papertrans.cn/m/image/620590.jpg
发表于 2025-3-26 10:10:47 | 显示全部楼层
发表于 2025-3-26 16:31:09 | 显示全部楼层
发表于 2025-3-26 20:27:24 | 显示全部楼层
Machine Learning for Computer Scientists and Data AnalystsFrom an Applied Pers
 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, 2025-5-13 09:03
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