상세정보

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.

• 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

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!

• 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

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.

1. Getting Ready to Unlock ML on Microcontrollers
2. Unleashing Your Creativity with Microcontrollers
3. Building a Weather Station with TensorFlow Lite for Microcontrollers
4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands
5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 1
6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico - Part 2
7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico
8. Classifying Desk Objects with TensorFlow and the Arduino Nano
9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico

(N.B. Please use the Look Inside option to see further chapters)

정보제공 :

Table of Contents
Getting Ready to Unlock ML on Microcontrollers
Unleashing Your Creativity with Microcontrollers
Building a Weather Station with TensorFlow Lite for Microcontrollers
Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 1
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 2
Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico
Classifying Desk Objects with TensorFlow and the Arduino Nano
Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico
Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU
Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM
Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and RaspberryPi Pico