Work through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learning
Key Features
Train and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi Pico
Work with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge Impulse
Explore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPU
Book DescriptionThis book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers.
The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you'll cover recipes relating to temperature, humidity, and the three "V" sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game.
By the end of this book, you'll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.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
Run an image classifier on a virtual Arm Ethos-U55 microNPU with microTVM
Who this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.
Sprache
Verlagsort
Zielgruppe
Maße
Höhe: 235 mm
Breite: 191 mm
Dicke: 19 mm
Gewicht
ISBN-13
978-1-80181-497-3 (9781801814973)
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 Klassifikation
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.
Table of Contents
Getting Started with TinyML
Prototyping with Microcontrollers
Building a Weather Station with TensorFlow Lite for Microcontrollers
Voice Controlling LEDs with Edge Impulse
Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano
Building a Gesture-Based Interface for YouTube Playback
Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS
Toward the Next TinyML Generation with microNPU
