Introduction to Transfer Learning : Algorithms and Practice, Paperback by Wan...

Description: Introduction to Transfer Learning : Algorithms and Practice, Paperback by Wang, Jindong; Chen, Yiqiang, ISBN 9811975868, ISBN-13 9789811975868, Like New Used, Free shipping in the US 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 th, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.

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Book Title: Introduction to Transfer Learning : Algorithms and Practice

Number of Pages: Xxi, 329 Pages

Language: English

Publication Name: Introduction to Transfer Learning : Algorithms and Practice

Publisher: Springer

Publication Year: 2024

Subject: Probability & Statistics / General, Intelligence (Ai) & Semantics, Computer Science, General

Type: Textbook

Author: Jindong Wang, Yiqiang Chen

Item Length: 9.3 in

Subject Area: Mathematics, Computers, Science

Series: Machine Learning: Foundations, Methodologies, and Applications Ser.

Item Width: 6.1 in

Format: Trade Paperback