TinyML Cookbook

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!

Type
ebook
Category
Data
publication date
2023-11-29
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

no of pages
664
duration
1328
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
approach
This is a practical guide that combines machine learning and embedded system to create pervasive and intelligent edge devices. You'll learn through practical step-by-step instructions that will give you hands-on experience while you understand the foundational concepts behind the practice
audience
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.
meta 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.
short description
With over 70 project-based recipes, the TinyML Cookbook is a practical guide that will help you to get the most out of your microcontrollers. It provides a comprehensive understanding of the theoretical foundations while giving you hands-on experience training ML models for deployment on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano microcontrollers.
subtitle
Combine machine learning with microcontrollers to solve real-world problems
keywords
Notemachine learning; raspberry pi pico; arduino nano 33 ble sense; sparkfun redboard artemis nano; tensorflow; microcontroller; single board computers; edge impulse; tvm; sensors
Product ISBN
9781837637362