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TinyML in Action: A Practical Guide to Machine Learning on Microcontrollers
Paperback
Publisher: Independently Published
Published: Nov 5 2025
Pages: 410
Weight: 1.56
Height: 0.84 Width: 7.00 Depth: 10.00
Language: English
TinyML in Action is your hands-on guide to making that future real. This comprehensive book takes you from the foundations of embedded machine learning to full, deployable AI projects running on ultra-low-power devices. Whether you're a developer, engineer, or curious maker, you'll learn to design, train, and deploy efficient neural networks that live right on your hardware.
Understand TinyML fundamentals: What it is, how it evolved, and why edge inference changes everything.
Master the hardware-software ecosystem: Learn to choose the right microcontroller (ARM Cortex-M, ESP32, Arduino Nano 33 BLE Sense) and sensors for your application.
Build and train real TinyML models: Use TensorFlow Lite for Microcontrollers, Edge Impulse, and CMSIS-NN to create compact, optimized neural networks.
Deploy, debug, and optimize models on-device: Convert models to C-arrays, manage tensor arenas, and achieve real-time inference even on devices with
Implement power-efficient designs: Learn duty cycling, quantization-aware training, and firmware optimization for long battery life.
Develop real-world edge AI projects: Gesture recognition, keyword spotting, image detection, predictive maintenance, and environmental monitoring-all step-by-step.
You'll walk through the entire TinyML workflow, from data → model → deployment, using practical, real-world examples grounded in official TensorFlow Lite Micro and Arduino references. Each chapter builds on the previous with structured learning: theory, implementation, optimization, and testing. You'll also find dedicated troubleshooting sections, hardware setup guides, and power-profiling strategies for dependable edge-AI performance.
By the end of this book, you'll know how to:
Collect and preprocess sensor data directly on your board.
Train compact neural networks using Python and TensorFlow/Keras.
Quantize, prune, and compress models for memory-limited devices.
Flash the compiled model and run inference in real time.
Profile latency, RAM usage, and power consumption with confidence.
Scale your TinyML applications with OTA updates and cloud integration via MQTT, AWS IoT, or Azure IoT Hub.
This book is perfect for:
Embedded developers exploring AI for the first time.
Machine learning practitioners looking to deploy models at the edge.
IoT engineers building intelligent sensors, wearables, or industrial monitors.
Students, educators, and makers passionate about sustainable, low-power AI.
No prior deep learning expertise is required - every example is practical, commented, and reproducible.
Inside You'll Build Projects LikeGesture recognition using an IMU sensor.
Keyword spotting wake-word detector.
Person detection on an ESP32-CAM.
Predictive maintenance system with vibration data.
Smart environmental monitor fusing sound, temperature, and motion.
Each project reinforces your understanding of embedded AI optimization, ensuring you can design models that think, sense, and respond - all within the constraints of a microcontroller.
Empower the edge. Code the future. Build the next generation of intelligent systems with TinyML.Start reading TinyML in Action today.
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