Introduction to Transfer Learning
- Indbinding:
- Hardback
- Sideantal:
- 352
- Udgivet:
- 31. marts 2023
- Udgave:
- 23001
- Størrelse:
- 160x25x241 mm.
- Vægt:
- 694 g.
- 8-11 hverdage.
- 7. december 2024
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- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Introduction to Transfer Learning
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.
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.
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Bogen Introduction to Transfer Learning findes i følgende kategorier:
- Business og læring > Computer og IT
- Databehandling og informationsteknologi > Informatik > Matematisk datateori
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Naturligt sprog og maskinoversættelse
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Machine learning
- Databehandling og informationsteknologi > Informatik > Kunstig intelligens > Computer vision
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