Tiny machine learning techniques for constrained devices
Éditeur :
Taylor & Francis Ltd
ISBN :
9781032897523
Date de publication :
29 janv. 2026
Dimensions :
23,4 x 15,6 cm
Poids :
590 g
Langue :
Anglais
Pays d'origine :
Grande Bretagne
Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of TinyML, enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge IoT nodes. It is a guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.