TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems, 2nd Edition

Over 70 recipes to help you develop smart applications on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano using the power of machine learning
Key Features

Train and deploy ML models on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano

Use different ML frameworks to unleash your microcontroller's potential

Explore 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 learn

Understand the relevant microcontroller programming fundamentals

Work with real-world sensors such as the microphone, camera, and accelerometer

Run on-device machine learning with TensorFlow Lite for Microcontrollers

Implement an app that responds to human voice with Edge Impulse

Leverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE Sense

Create a gesture-recognition app with Raspberry Pi Pico

Design a CIFAR-10 model for memory-constrained microcontrollers

Build 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 Contents

Getting Started With TinyML

Prototyping with microcontrollers

Building A Snow Weather Station with TensorFlow Lite for Microcontrollers

Voice Controlling LEDs with Edge Impulse and Arduino Nano

Music genre recognition with TensorFlow and Raspberry Pi Pico

Object detection with Edge Impulse using FOMO and Raspberry Pi Pico

Indoor Scene Classification with TensorFlow and Arduino Nano

Gesture-Based Interface for YouTube Playback with Edge Impulse and Raspberry Pi Pico

Running a CIFAR-10 model for memory constrained devices on QEMU with ZephyrOS

Building a tinyML application with TVM on Arduino Nano and Arm Ethos-U microNPU

Enabling on-device training on Raspberry Pi Pico to unleash the full potential of TinyML