TinyML Cookbook - Second Edition: Combine machine learning with microcontrollers to solve real-world problems

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:

Over 20+ new recipes, including recognizing music genres and detecting objects in a scene

Create practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and more

Explore cutting-edge technologies, such as on-device training for updating models 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 the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano.

TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP.

This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you'll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!

What You Will Learn:

Understand the microcontroller programming fundamentals

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

Run on-device ML with TensorFlow Lite for Microcontrollers

Implement an app that responds to human voice with Edge Impulse

Leverage transfer learning with FOMO and Keras

Squeeze ML models into tight memory with quantization and other optimization methods

Create gesture-recognition and music genre classifier apps with the Raspberry Pi Pico

Design a CIFAR-10 model for memory-constrained microcontrollers

Who this book is for:

This book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you're an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion.

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.

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