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Embedded TinyML: A Hands-on Guide to Deploying Intelligent and Advanced AI on Resource-Constrained Microcontrollers
Paperback
Publisher: Independently Published
Published: Nov 28 2025
Pages: 228
Weight: 0.89
Height: 0.48 Width: 7.00 Depth: 10.00
Language: English
Bridge the gap between Python-based Machine Learning and C++ Embedded Systems with this comprehensive, hands-on guide to TensorFlow Lite for Microcontrollers.
Are you a Data Scientist who feels limited by the cloud? Are you a Firmware Engineer ready to add intelligence to your devices?
We are living through a quiet revolution. While the world focuses on massive Large Language Models in server farms, a more pervasive shift is happening at the edge. Intelligence is moving from the cloud to the sensor, enabling devices to see, hear, and feel without internet connectivity, latency, or privacy concerns.
Embedded TinyML is your field guide to this new frontier.
This book is not a theoretical treatise. It is a rigorous engineering roadmap designed to take you from the physics of silicon to the deployment of quantized neural networks on resource-constrained microcontrollers.
Using industry-standard hardware like the ESP32, Arduino Nano 33 BLE Sense, and STM32, you will learn to build systems that run on coin-cell batteries for years.
What You Will Learn:
The Philosophy of Constraints: How to turn memory limits (kB) and clock speeds (MHz) into drivers for efficient engineering.
The Hardware Stack: Deep dives into ARM Cortex-M architecture, DSPs, and NPUs.
Energy Profiling: Master power management strategies to measure and minimize consumption per inference.
Model Optimization: A complete breakdown of Quantization (Int8 vs Float32), Pruning, and Architecture Search.
TensorFlow Lite Micro: Navigate the TFLite ecosystem, from training in Keras/Python to C++ deployment.
Build Four Real-World Projects:
1. Proprioception: Build a multi-class gesture recognition wand using IMU sensor fusion.
2. Vision: Create a privacy-preserving Visual Wake-Word detector on low-res camera modules.
3. Industrial IoT: Develop an unsupervised Anomaly Detection system for predictive maintenance on vibrating machinery.
4. Voice Interface: Engineer a two-stage keyword spotting pipeline for voice control.
Whether you are building a smart home device, a health wearable, or an industrial sensor, this book provides the code, the theory, and the strategy to deploy AI where it matters most: at the edge.
Stop uploading raw data. Start deploying intelligence.
Scroll up and grab your copy today to join the TinyML revolution.
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