E-Books →TinyML Cookbook Combine machine learning with microcontrollers to solve real–world problems, 2nd Edition

Key FeaturesTrain and deploy ML models on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis NanoUse different ML frameworks to unleash your microcontroller's potentialExplore cutting-edge technologies, such as on-device training to update a model without data leaving the device
Book Description
Discover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano.
Whether you are new to this field or looking to expand your machine learning knowledge, this book has something for everyone. You'll start with a practical introduction to this multidisciplinary field to get you up to speed with deploying intelligent applications on microcontrollers and will tackle various problems.
Next, you'll focus on temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to implement end-to-end smart applications in different scenarios. You'll learn best practices for building tiny models for memory-constrained microcontrollers.
This edition includes new recipes featuring an LSTM neural network to recognize music genres and the Edge Impulse Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. These will help you stay up to date with the latest developments in the tinyML community.
Finally, take your tinyML solutions to the next level with microTVM, microNPU, and on-device learning. This book will give you the knowledge to make the most of your microcontroller and create unique projects with tinyML!
What you will learnUnderstand the relevant microcontroller programming fundamentalsWork with real-world sensors such as the microphone, camera, and accelerometerRun on-device machine learning with TensorFlow Lite for MicrocontrollersImplement an app that responds to human voice with Edge ImpulseLeverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE SenseCreate a gesture-recognition app with Raspberry Pi PicoDesign a CIFAR-10 model for memory-constrained microcontrollersBuild a music genre classifier with the Raspberry Pi Pico
Who This Book Is For
If you're a machine learning enthusiast or data scientist interested in developing machine learning applications on microcontrollers through practical examples, this book is for you. Embedded developers with little familiarity with ML can also find plenty of useful information in these recipes.
Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book
Table of ContentsGetting Started With TinyMLPrototyping with microcontrollersBuilding A Snow Weather Station with TensorFlow Lite for MicrocontrollersVoice Controlling LEDs with Edge Impulse and Arduino NanoMusic genre recognition with TensorFlow and Raspberry Pi PicoObject detection with Edge Impulse using FOMO and Raspberry Pi PicoIndoor Scene Classification with TensorFlow and Arduino NanoGesture-Based Interface for YouTube Playback with Edge Impulse and Raspberry Pi PicoRunning a CIFAR-10 model for memory constrained devices on QEMU with ZephyrOSBuilding a tinyML application with TVM on Arduino Nano and Arm Ethos-U microNPUEnabling on-device training on Raspberry Pi Pico to unleash the full potential of TinyML