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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

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