TinyML Cookbook
Combine machine learning with microcontrollers to solve real-world problems
2nd Edition
Published on 29. November 2023
Book
Paperback/Softback
664 pages
978-1-83763-736-2 (ISBN)
Description
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
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 DescriptionDiscover 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
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 forThis 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.
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
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 DescriptionDiscover 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
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 forThis 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.
More details
Edition
2nd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 36 mm
Weight
1219 gr
ISBN-13
978-1-83763-736-2 (9781837637362)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
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.
Content
Table of Contents
Getting Ready to Unlock ML on Microcontrollers
Unleashing Your Creativity with Microcontrollers
Building a Weather Station with TensorFlow Lite for Microcontrollers
Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 1
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2
Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico
Classifying Desk Objects with TensorFlow and the Arduino Nano
Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico
Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU
Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM
Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and RaspberryPi Pico
Getting Ready to Unlock ML on Microcontrollers
Unleashing Your Creativity with Microcontrollers
Building a Weather Station with TensorFlow Lite for Microcontrollers
Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 1
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2
Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico
Classifying Desk Objects with TensorFlow and the Arduino Nano
Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico
Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU
Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM
Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and RaspberryPi Pico