Transfer Learning
This edited volume explores the potential of transfer learning in advancing artificial intelligence (AI) across diverse domains. Transfer learning enables AI systems to leverage knowledge gained from one task to enhance performance in another, significantly reducing data requirements and training ti...
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| Формат: | Online |
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| Хэл сонгох: | англи |
| Хэвлэсэн: |
IntechOpen
2025
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| Нөхцлүүд: | |
| Онлайн хандалт: | ONIX_20250617T171318_9780850142471_170 |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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| _version_ | 1869520713998663680 |
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| collection | Directory of Open Access Books |
| description | This edited volume explores the potential of transfer learning in advancing artificial intelligence (AI) across diverse domains. Transfer learning enables AI systems to leverage knowledge gained from one task to enhance performance in another, significantly reducing data requirements and training time while improving model efficiency. The book presents the latest approaches for implementing transfer learning in various contexts, from telecommunications and brain-computer interfaces to quantum computing applications. Readers will discover innovative techniques for domain adaptation, cross-domain knowledge transfer, and hybrid classical-quantum implementations. The text addresses critical challenges in making transfer learning more explainable, reliable, and scalable, particularly concerning privacy preservation and computational efficiency. Key topics include AI-native networks, neural network transfer learning, domain adaptation strategies, and quantum machine learning integration. Both theoretical frameworks and practical implementations are discussed, making this book valuable for researchers, practitioners, and students interested in developing more efficient and capable AI systems. The content bridges the gap between theoretical understanding and practical application, offering insights into how transfer learning can be effectively deployed in real-world scenarios. By examining transfer learning through multiple lenses, from traditional neural networks to quantum computing, this volume provides a unique perspective on the future of AI development and its potential to revolutionize various technological sectors. |
| format | Online |
| id | doab-20.500.12854ir-161598 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1615982025-06-17T15:43:17Z Transfer Learning Abdul Majeed, Anwar P.P. Mathematical theory of computation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation This edited volume explores the potential of transfer learning in advancing artificial intelligence (AI) across diverse domains. Transfer learning enables AI systems to leverage knowledge gained from one task to enhance performance in another, significantly reducing data requirements and training time while improving model efficiency. The book presents the latest approaches for implementing transfer learning in various contexts, from telecommunications and brain-computer interfaces to quantum computing applications. Readers will discover innovative techniques for domain adaptation, cross-domain knowledge transfer, and hybrid classical-quantum implementations. The text addresses critical challenges in making transfer learning more explainable, reliable, and scalable, particularly concerning privacy preservation and computational efficiency. Key topics include AI-native networks, neural network transfer learning, domain adaptation strategies, and quantum machine learning integration. Both theoretical frameworks and practical implementations are discussed, making this book valuable for researchers, practitioners, and students interested in developing more efficient and capable AI systems. The content bridges the gap between theoretical understanding and practical application, offering insights into how transfer learning can be effectively deployed in real-world scenarios. By examining transfer learning through multiple lenses, from traditional neural networks to quantum computing, this volume provides a unique perspective on the future of AI development and its potential to revolutionize various technological sectors. 2025-06-17T15:43:13Z 2025-06-17T15:43:13Z 2025 book ONIX_20250617T171318_9780850142471_170 2633-1403 9780850142471 9780850142464 9780850142488 https://directory.doabooks.org/handle/20.500.12854/161598 eng Artificial Intelligence image/jpeg n/a https://www.intechopen.com/books/13395 https://mts.intechopen.com/storage/books/13395/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/intechopen.114788 10.5772/intechopen.114788 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9780850142471 9780850142464 9780850142488 IntechOpen 32 126 open access |
| spellingShingle | Mathematical theory of computation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation Transfer Learning |
| title | Transfer Learning |
| title_full | Transfer Learning |
| title_fullStr | Transfer Learning |
| title_full_unstemmed | Transfer Learning |
| title_short | Transfer Learning |
| title_sort | transfer learning |
| topic | Mathematical theory of computation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation |
| topic_facet | Mathematical theory of computation thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation |
| url | ONIX_20250617T171318_9780850142471_170 |