Free Download TinyML Cookbook
by Gian Marco Iodice
English | 2023 | ISBN: 1837637369 | 665 pages | True PDF EPUB | 49.96 MB
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
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
Book 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 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!
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 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.
Table of Contents
Getting Started With TinyML
Prototyping with microcontrollers
Building A Snow Weather Station with TensorFlow Lite for Microcontrollers
Voice Controlling LEDs with Edge Impulse and Arduino Nano
Music genre recognition with TensorFlow and Raspberry Pi Pico
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2
Object detection with Edge Impulse using FOMO and Raspberry Pi Pico
Indoor Scene Classification with TensorFlow and Arduino Nano
Gesture-Based Interface for YouTube Playback with Edge Impulse and Raspberry Pi Pico
Running a CIFAR-10 model for memory constrained devices on QEMU with ZephyrOS
Building a tinyML application with TVM on Arduino Nano and Arm Ethos-U microNPU
(N.B. Please use the Look Inside option to see further chapters)
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