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Tiny Machine Learning Techniques for Constrained Devices|Khalid El-Makkaoui

Tiny Machine Learning Techniques for Constrained Devices

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Overview

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.

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Details

  • ISBN-13: 9781032897523
  • ISBN-10: 103289752X
  • Publisher: CRC Press
  • Publish Date: January 2026
  • Dimensions: 9.21 x 6.14 x 0.56 inches
  • Shipping Weight: 1.11 pounds
  • Page Count: 224

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