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
Purchase of the print or Kindle book includes a free eBook in PDF format.
Key Features
- Over 20+ new recipes, including recognizing music genres and detecting objects in a scene
- Run on-device ML with TensorFlow Lite for Microcontrollers, Edge Impulse, TVM, and scikit-learn
- 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 take your tinyML solutions to the next level with microTVM, microNPU, scikit-learn, and on-device learning. This 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
- Implement an app that responds to human voice or recognizes music genres
- Leverage transfer learning with FOMO and Keras
- Learn best practices on how to use the CMSIS-DSP library
- Create a gesture-recognition app to build a remote control
- Design a CIFAR-10 model for memory-constrained microcontrollers
- Train a neural network on 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.
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
- Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2
- 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
(N.B. Please use the Look Inside option to see further chapters)
Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones.
Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs.
In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.
