Overview

Big AI. Tiny Hardware.The cloud is too slow. The cloud is too expensive. It's time to put the brain directly on the chip. For years, ""Artificial Intelligence"" meant massive GPUs and server farms. But the next revolution isn't happening in a data center-it's happening on a microcontroller smaller than your thumbnail. Edge AI for IoT Devices is the definitive guide to TinyML: the art of running machine learning models on constrained hardware with kilobytes of memory, not gigabytes. This book is for the embedded engineer who wants to add intelligence to their products without adding cost, and for the data scientist who wants to deploy their models to the physical edge. You will learn to squeeze neural networks onto devices like the ESP32, Arduino Nano 33 BLE, and STM32, enabling them to see, hear, and feel in real-time. Intelligence, Unplugged.From ""Hey Google"" style voice recognition to industrial predictive maintenance, this guide covers the full workflow of embedded machine learning. The TinyML Workflow: Learn the end-to-end process of collecting data, training a model in TensorFlow, and converting it to C++ for deployment on bare metal. Extreme Optimization: Master Quantization (turning 32-bit floats into 8-bit integers) and Pruning to make your models 90% smaller and faster without losing accuracy. Sensor Intelligence: Build projects that use accelerometers for gesture recognition and microphones for keyword spotting. Anomaly Detection: Create systems that ""learn"" what normal vibration looks like on a motor and trigger an alert before the machine breaks-all locally. Energy Management: Techniques to duty-cycle your AI inference so your smart device can run for months on a coin cell battery. Whether you are building a smart wearable, a remote wildlife camera, or an industrial sensor node, this book gives you the tools to sever the connection to the cloud. Stop sending data. Start sending insights. Scroll up and get your copy to master the cutting edge of Edge AI.

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