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

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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
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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.
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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: ..
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Machine Learning: Foundations, Methodologies, and Applicationshttp://image.papertrans.cn/i/image/474290.jpg
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https://doi.org/10.1007/978-981-19-7584-4Transfer learning; Domain adaption; Domain generalization; Meta-learning; Transfer of learning; Knowledge
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