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Tiny Machine Learning Techniques for Constrained Devices
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.
| Mediji | Grāmatas Hardcover Book (Grāmata ar cieto muguriņu un vāku) |
| Izlaists | 2026. gada 29. janvāris |
| ISBN13 | 9781032897523 |
| Izdevēji | Taylor & Francis Ltd |
| Lapas | 224 |
| Izmēri | 150 × 220 × 20 mm · 590 g |
| Valoda | Angļu |
| Redaktors | Abd El-Latif, Ahmed A. |
| Redaktors | El-Makkaoui, Khalid |
| Redaktors | Lamaakal, Ismail |
| Redaktors | Maleh, Yassine |
| Redaktors | Ouahbi, Ibrahim |