Introduction to Transfer Learning: Algorithms and Practice
暫譯: 轉移學習入門：演算法與實務

Wang, Jindong, Chen, Yiqiang

Introduction to Transfer Learning: Algorithms and Practice
  • 出版商: Springer
  • 出版日期: 2024-10-19
  • 售價: $2,400
  • 貴賓價: 9.5 折 $2,280
  • 語言: 英文
  • 頁數: 329
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9811975868
  • ISBN-13: 9789811975868
  • 相關分類: Algorithms-data-structures

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.


商品描述(中文翻譯)

轉移學習是人工智慧和深度學習時代中最重要的技術之一。它旨在通過將現有知識轉移到另一個新領域來利用已有的知識。多年來，許多相關主題吸引了研究和應用社群的興趣：轉移學習、預訓練與微調、領域適應、領域泛化以及元學習。

本書提供了轉移學習的全面教程，向該領域的新研究者介紹經典和較新的演算法。最重要的是，它從「學生」的角度介紹所有概念、理論、演算法和應用，讓讀者能夠快速且輕鬆地進入這個領域。隨書附有詳細的程式碼實現，以更好地說明幾個重要演算法的核心思想，提供良好的實踐範例。

作者簡介

Jindong Wang is currently a senior researcher at Microsoft Research Asia. Before that, he obtained his PhD from the Institute of Computing Technology, Chinese Academy of Sciences, in 2019. His main research interests are in transfer learning, domain adaptation, domain generalization, and their applications in ubiquitous computing systems. He has co-published a Chinese-language textbook, Introduction to Transfer Learning, and numerous papers in leading journals and conferences, such as the IEEE TKDE, TNNLS, ACM TIST, NeurIPS, CVPR, IJCAI, UbiComp, and ACMMM. He was awarded the best application paper at the IJCAI'19 federated learning workshop and best paper at ICCSE'18. He has served as the publicity chair of IJCAI'19 and the transfer learning session chair of ICDM'19.

Yiqiang Chen is currently a professor at the Institute of Computing Technology, Chinese Academy of Sciences. His main research interests are in artificial intelligence and pervasive computing. He has published more than 180 papers in leading journals and conferences such as the IEEE TKDE, AAAI, and IJCAI. He has served as the general PC chair of the IEEE UIC 2019, PCC 2017, and CWCC 2019. He is a founding committee member of the IEEE wearable and intelligent interaction committee (IWCD) and an associate editor for IEEE TETCI and IJMLC. He has won several best paper awards, including best application paper at IJCAI-FL'19, IJIT 15th anniversary best paper award, and ICCSE'18 best paper award.


作者簡介(中文翻譯)

金東王目前是微軟亞洲研究院的高級研究員。在此之前，他於2019年獲得中國科學院計算技術研究所的博士學位。他的主要研究興趣包括轉移學習、領域適應、領域泛化及其在無處不在計算系統中的應用。他共同出版了一本中文教材《轉移學習導論》，並在《IEEE TKDE》、《TNNLS》、《ACM TIST》、NeurIPS、CVPR、IJCAI、UbiComp和ACMMM等頂尖期刊和會議上發表了多篇論文。他在IJCAI'19聯邦學習研討會上獲得最佳應用論文獎，並在ICCSE'18獲得最佳論文獎。他曾擔任IJCAI'19的宣傳主席及ICDM'19的轉移學習分會主席。

陳毅強目前是中國科學院計算技術研究所的教授。他的主要研究興趣包括人工智慧和普及計算。他在《IEEE TKDE》、《AAAI》和IJCAI等頂尖期刊和會議上發表了超過180篇論文。他曾擔任IEEE UIC 2019、PCC 2017和CWCC 2019的總程序委員會主席。他是IEEE可穿戴與智能互動委員會（IWCD）的創始委員會成員，並擔任《IEEE TETCI》和《IJMLC》的副編輯。他獲得了多個最佳論文獎，包括IJCAI-FL'19的最佳應用論文獎、《IJIT》15週年最佳論文獎和ICCSE'18最佳論文獎。

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