找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Introduction to Transfer Learning; Algorithms and Pract Jindong Wang,Yiqiang Chen Book 2023 The Editor(s) (if applicable) and The Author(s)

[復(fù)制鏈接]
查看: 22344|回復(fù): 51
樓主
發(fā)表于 2025-3-21 16:46:27 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Introduction to Transfer Learning
副標(biāo)題Algorithms and Pract
編輯Jindong Wang,Yiqiang Chen
視頻videohttp://file.papertrans.cn/475/474290/474290.mp4
概述Fast and painless icebreaker for your journey into transfer learning.Clear summaries of both classic and more recent algorithms.Complementary source codes for good practice examples
叢書名稱Machine Learning: Foundations, Methodologies, and Applications
圖書封面Titlebook: Introduction to Transfer Learning; Algorithms and Pract Jindong Wang,Yiqiang Chen Book 2023 The Editor(s) (if applicable) and The Author(s)
描述.Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning...?This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice..
出版日期Book 2023
關(guān)鍵詞Transfer learning; Domain adaption; Domain generalization; Meta-learning; Transfer of learning; Knowledge
版次1
doihttps://doi.org/10.1007/978-981-19-7584-4
isbn_softcover978-981-19-7586-8
isbn_ebook978-981-19-7584-4Series ISSN 2730-9908 Series E-ISSN 2730-9916
issn_series 2730-9908
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

書目名稱Introduction to Transfer Learning影響因子(影響力)




書目名稱Introduction to Transfer Learning影響因子(影響力)學(xué)科排名




書目名稱Introduction to Transfer Learning網(wǎng)絡(luò)公開度




書目名稱Introduction to Transfer Learning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Introduction to Transfer Learning被引頻次




書目名稱Introduction to Transfer Learning被引頻次學(xué)科排名




書目名稱Introduction to Transfer Learning年度引用




書目名稱Introduction to Transfer Learning年度引用學(xué)科排名




書目名稱Introduction to Transfer Learning讀者反饋




書目名稱Introduction to Transfer Learning讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 22:28:18 | 只看該作者
板凳
發(fā)表于 2025-3-22 03:30:20 | 只看該作者
地板
發(fā)表于 2025-3-22 05:41:27 | 只看該作者
Transfer Learning for Computer Vision” example of deep learning tutorial is MNIST digits classification and the ImageNet challenge has dramatically boosted the rapid of deep learning. To now, ImageNet is still the common benchmark in many areas.
5#
發(fā)表于 2025-3-22 09:58:21 | 只看該作者
Transfer Learning for Natural Language Processing role in common NLP tasks. In this chapter, we show how to perform fine-tuning using the pre-trained language model on a sentence classification task. To save space, we will only introduce the important code snippets in this chapter. For complete code, please refer to the link: ..
6#
發(fā)表于 2025-3-22 14:36:59 | 只看該作者
7#
發(fā)表于 2025-3-22 20:07:32 | 只看該作者
Machine Learning: Foundations, Methodologies, and Applicationshttp://image.papertrans.cn/i/image/474290.jpg
8#
發(fā)表于 2025-3-22 23:13:47 | 只看該作者
https://doi.org/10.1007/978-981-19-7584-4Transfer learning; Domain adaption; Domain generalization; Meta-learning; Transfer of learning; Knowledge
9#
發(fā)表于 2025-3-23 02:16:26 | 只看該作者
10#
發(fā)表于 2025-3-23 07:24:27 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2026-1-22 04:12
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
正定县| 香港 | 仙居县| 锡林浩特市| 酒泉市| 保靖县| 菏泽市| 黔东| 临夏市| 长丰县| 贡嘎县| 江山市| 九龙坡区| 井陉县| 盐池县| 西和县| 清涧县| 临澧县| 新邵县| 云安县| 卫辉市| 仁布县| 屏边| 河北省| 金昌市| 遵化市| 金湖县| 城步| 磐安县| 四平市| 宁南县| 正定县| 六盘水市| 钟祥市| 宜都市| 南召县| 余庆县| 罗甸县| 通道| 永福县| 长武县|