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

Gian Marco Iodice

€32.99

4.8 (18 Ratings)

Paperback Nov 2023 664 pages 2nd Edition

Gian Marco Iodice

€32.99

4.8 (18 Ratings)

Paperback Nov 2023 664 pages 2nd Edition

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TinyML Cookbook

Unleashing Your Creativity with Microcontrollers

Bringing machine learning (ML) to life on microcontrollers is a thrilling adventure because our creations can go beyond our computers’ boundaries and make an impact in the real world. However, before diving into this fascinating world, let’s take a moment to explore how to craft basic applications on microcontrollers to get up to speed with the principles of embedded programming.

In this chapter, we will start our exploration by handling data transmission over the serial communication protocol, equipping ourselves with a foundation for basic code debugging. The transmitted data will be captured in a log file and uploaded to our cloud storage in Google Drive.

Afterward, we will delve into programming the GPIO peripheral using the Arm Mbed API and use a solderless breadboard to connect external components, such as LEDs and push-buttons.

The aim of this chapter is to delve into the basic principles of microcontroller...

Transmitting data over serial communication

Code debugging is a fundamental process of software development to identify errors in code.

This recipe will demonstrate how to conduct print debugging on the Arduino Nano and Raspberry Pi Pico by transmitting the following strings to the serial terminal:

Initialization completed: once the serial port on the microcontroller has finished initializing
Executed: after every 2 seconds of program execution

Getting ready

All programs are prone to bugs, and print debugging is a basic process that displays statements on the output terminal, providing valuable insight into the program’s execution, as shown in the following example:

int func (int func_type, int a) {
  int ret_val = 0;
  switch(func_type){
    case 0:
      printf("FUNC0\n");
      ret_val = func0(a)
      break;
    default:
      printf("FUNC1\n");
      ret_val = func1(a);
  }
  return ret_val;
}

In the preceding...

Download Code

Key benefits

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

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!

Who is this book 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.

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

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Kam F Siu Jan 30, 2024

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N/A Jan 29, 2024

Je trouve que le contenu du livre est claire concis et pas du tout compliqué e merci beaucoup...

Jean Labbe Sep 09, 2024

Well Donne! Excellent for beginners. Explanations are clear and easy to follow. Illustrations are very useful with all steps.

Mark D Dec 01, 2023

Having read the first edition of this book that I own, I received a pre-release copy of the second edition from <PACKT> to review for this book. I was a co-editor on another <PACKT> book related to RTOS (Real-time Operating Systems) so I get pre-release copies from time to time to review.This book is a great expansion of the first edition and includes more visual diagrams and expanded detail to explain hardware connectivity, MEMS sensors and how they operate, different types of machine learning inference with sensor devices, the Edge Impulse cloud-based no-code machine learning toolkit, and Tensorflow programming using the Arduino IDE.I wouldn't consider this book for absolute beginners but a beginner would need to read it a couple of times first to understand core concepts before trying to do the "How To Do It" sections at the end of each example project. This book is more suited with someone who has some exposure to embedded microcontroller programming with Arduino IDE, Arduino dev boards like the Nano 33 BLE Sense, the Raspberry Pi Pico dev board, and perhaps the ESP32 dev board variants from Espressif Systems.The new Arduino Nano 33 BLE Sense 2 has recently come out and should apply to this book as well for the Edge Impulse and Tensorflow chapters for deploying TinyML machine learning models. If you buy this book now and buy an Arduino Nano 33 BLE Sense dev board and peripherals for Christmas, you can have enough time to read the book and deploy TinyML models over the Christmas holidays after your dev board arrives!I work with embedded machine learning on intelligent wireless IoT devices for my business and can deploy TinyML models to almost any ARM Cortex-M embedded microcontroller out there. I use other machine learning tools to deploy TinyML models directly onto MEMS sensors as well.Gian Marco Iodice is an expert in the field of embedded machine learning due to his work at ARM in the UK and his education experience in researching the field of TinyML on embedded systems or resource-constrained embedded devices for computer vision. The principles of this book cover a wide range of TinyML possibilities with great examples from deploying machine learning models from scratch using the Arduino IDE with C and C++ code and ARM MBED OS to no-code tools like Edge Impulse.For anyone wanting to learn how to deploy machine learning models to an embedded microcontroller development kit like the Arduino Nano 33 Ble Sense or the Raspberry Pi Pico dev kit, you must get this book to learn how to do it easily while learning important concepts at the same time. You can also join the "Embedded Systems Professionals" Discord channel to ask the author of the book, Gian Mardo Iodice, questions about the contents of the book and to get some help on how to deploy TinyML models to your dev board.To conclude, I know you will enjoy the book as much as I did. The second edition is an improvement to the first edition with updated code fixes, more diagrams, expanded explanations of topics, and updated information. Buy an Arduino Nano 33 BLE Sense dev board, buy some peripheral sensors to connect to your Arduino dev board, and start deploying TinyML models with the "TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems" today. I highly recommend this book if you want to learn about the future of machine learning on embedded devices and how to actually deploy TinyML models onto embedded systems to make those systems really smart.

Amazon Verified review

Heena Chouhan Feb 07, 2024

If you're into microcontrollers and machine learning like I am, this book is an absolute gem. It's the perfect fusion of both worlds, providing valuable insights on how to leverage machine learning to tackle real-world challenges on power and compute-constrained devices.

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

What do you get with Print?

TinyML Cookbook

Unleashing Your Creativity with Microcontrollers

Technical requirements

Transmitting data over serial communication

Getting ready

Reading serial data and uploading files to Google Drive with Python

Getting ready

There’s more…

Implementing an LED status indicator on the breadboard

Getting ready

Controlling an external LED with the GPIO

Getting ready

Understanding the LED functionality

Turning an LED on and off with a push-button

Getting ready

The operating principles of the push-button

Using interrupts to read the push-button state

Getting ready

Working with interrupts using the Mbed OS API

Summary

Learn more on Discord

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