El-Makkaoui K Tiny Machine Learning Techniques for Constrained Devices 2026
Posted: Wed Jan 07, 2026 2:35 pm
El-Makkaoui K Tiny Machine Learning Techniques for Constrained Devices 2026 | 22.88 MB
Title: Tiny Machine Learning Techniques for Constrained Devices
Author: Khalid El-Makkaoui;Ismail Lamaakal;Ibrahim Ouahbi;Yassine Maleh;Ahmed A. Abd El-Latif;
Description:
Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.
This book offers thorough coverage of key topics, including:
- Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints.
- Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies.
- Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices.
- Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation.
- Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students.
Download From Below Rapidgator & Nitroflare Links
Please Login or Register to see this code Please Login or Register to see this code