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Tiny Machine Learning Techniques for Constrained Devices
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Tiny Machine Learning Techniques for Constrained Devices
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.
| Medios de comunicación | Libros Hardcover Book (Libro con lomo y cubierta duros) |
| Publicado | 29 de enero de 2026 |
| ISBN13 | 9781032897523 |
| Editores | Taylor & Francis Ltd |
| Páginas | 224 |
| Dimensiones | 150 × 220 × 20 mm · 590 g |
| Lengua | Inglés |
| Editor | Abd El-Latif, Ahmed A. |
| Editor | El-Makkaoui, Khalid |
| Editor | Lamaakal, Ismail |
| Editor | Maleh, Yassine |
| Editor | Ouahbi, Ibrahim |