Overview

TinyML for Beginners is a modern, hands-on introduction to building real, production-grade machine-learning systems on microcontrollers. Designed for absolute beginners and fast-moving engineers, this book teaches you how to bring AI directly to the edge using low-power devices such as ESP32, Raspberry Pi Pico (RP2040), and STM32 microcontrollers-without relying on cloud servers or expensive hardware. This practical guide walks you step by step through the complete TinyML workflow: collecting sensor data, building optimized models, quantizing them for microcontroller limits, deploying them through TensorFlow Lite Micro, Edge Impulse, and STM32Cube.AI, and integrating them into real-time smart home automations. Every chapter includes hands-on labs with measurable outcomes, deployable code examples, and clear implementation patterns used in real embedded AI systems. Readers learn how to design reliable on-device models for gesture recognition, keyword spotting, environmental prediction, and vibration-based anomaly detection. The book also provides proven solutions to common TinyML pain points such as quantization accuracy loss, tensor arena overflows, preprocessing drift, memory constraints, sensor noise, deployment crashes, and slow inference latency. You will build and deploy a complete full-stack TinyML smart home system-featuring multiple AI-powered nodes communicating via Wi-Fi, BLE, or MQTT-and learn how to monitor performance, reduce power consumption, perform OTA updates, and validate models in real environments. Whether you are a beginner exploring edge AI for the first time or an engineer building low-power intelligent devices, this book provides a fast, clear, and practical blueprint for success with modern TinyML. Learn how to: Build real TinyML models using Edge Impulse, TensorFlow Lite Micro, and STM32Cube.AI Collect, label, and preprocess audio, IMU, vibration, and environmental datasets Train and optimize models using quantization, pruning, and CMSIS-NN acceleration Deploy ML inference loops on ESP32, RP2040, and STM32 microcontrollers Integrate microcontroller-based AI with Wi-Fi, BLE, MQTT, Node-RED, and Home Assistant Debug accuracy loss, latency spikes, operator mismatches, and memory overflows Profile power consumption and design energy-efficient sensing loops Build full smart home automations triggered by TinyML predictions Implement field testing, concept-drift handling, and model lifecycle updates Deploy production-ready firmware with stable sensor pipelines and OTA support What makes this TinyML book different? 100% practical, hands-on, project-driven Uses modern 2024-2025 toolchains, libraries, and hardware Covers real-world pain points and how to solve them reliably Includes chapter-based practice labs and a complete full-stack capstone project Written to be beginner-friendly but technically accurate, complete, and professional Fully aligned with how TinyML is deployed today in smart homes, IoT, wearables, and embedded systems If you want the fastest, clearest path to building real on-device AI systems-with modern tools, optimized models, reliable deployments, and practical smart home projects-this book is the perfect starting point.

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