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TinyML on Microcontrollers: Running Neural Networks on Resource- Constrained Hardware

TinyML on Microcontrollers: Running Neural Networks on Resource- Constrained Hardware


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About the Book

TinyML is bringing machine learning to the smallest devices-microcontrollers. Imagine running neural networks on a battery-powered sensor, a wearable device, or a smart appliance, all without needing a cloud connection. This book shows you how to implement efficient models that fit in the tightest of spaces, even with limited processing power and memory. TinyML on Microcontrollers is a hands-on guide that walks you through designing, deploying, and optimizing machine learning models on resource-constrained hardware. You'll learn how to build custom models for real-time inference, develop efficient data pipelines, and fine-tune models for performance and memory usage. From training on full datasets to converting models for deployment using quantization and pruning, this book covers all the essential techniques. You'll work through step-by-step projects using popular microcontroller platforms like Arduino, Raspberry Pi, and TensorFlow Lite for Microcontrollers. The book also covers tools like Edge Impulse for model development and deployment, providing you with the skills to move from prototypes to fully functional devices. Topics like sensor integration, low-power consumption, and optimizing code to run on minimal resources make this a practical, real-world guide. By the end of this book, you'll be able to take machine learning from the cloud to the edge, running powerful neural networks on small, cost-effective microcontrollers. Buy this book now and start building TinyML applications that bring intelligent systems to life, even in the most resource-constrained environments.


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Product Details
  • ISBN-13: 9798298434256
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 229 mm
  • No of Pages: 146
  • Returnable: N
  • Sub Title: Running Neural Networks on Resource- Constrained Hardware
  • Width: 152 mm
  • ISBN-10: 8298434250
  • Publisher Date: 16 Aug 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 8 mm
  • Weight: 258 gr


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