Buch, Englisch, 265 Seiten, Format (B × H): 173 mm x 246 mm, Gewicht: 649 g
Reihe: Synthesis Lectures on Learning, Networks, and Algorithms
Framework, Foundation, and Algorithm Design
Buch, Englisch, 265 Seiten, Format (B × H): 173 mm x 246 mm, Gewicht: 649 g
Reihe: Synthesis Lectures on Learning, Networks, and Algorithms
ISBN: 978-3-031-84362-4
Verlag: Springer Nature Switzerland
This book provides a comprehensive introduction to continual and reinforcement learning for edge AI, which investigates how to build an AI agent that can continuously solve new learning tasks and enhance the AI at resource-limited edge devices. The authors introduce readers to practical frameworks and in-depth algorithmic foundations. The book surveys the recent advances in the area, coming from both academic researchers and industry professionals. The authors also present their own research findings on continual and reinforcement learning for edge AI. The book also covers the practical applications of the topic and identifies exciting future research opportunities.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
Weitere Infos & Material
Introduction to Continual and Reinforcement Learning for Edge AI.- Algorithmic and Theoretical Foundations.- Federated Continual Learning.- On-device Continual Learning.- Online Meta-Learning.- Warm-start Reinforcement Learning.- Continual Reinforcement Learning.- Continual and Reinforcement Learning for Edge AI with Pre-trained Large Language Models.