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:

• Train, optimize, and deploy ML models using TensorFlow Lite and Edge Impulse
• Get to grips with embedded platforms like Arm Mbed OS and Zephyr OS and peripherals like GPIO and I2C
• 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 Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano.

You'll learn the unique constraints of on-device ML and how to work with embedded platforms like Arm Mbed OS. TinyML Cookbook, Second Edition, will show you how to implement end-to-end smart applications in different scenarios using the three "V" sensors (Voice, Vision, and Vibration). You'll train custom models from weather prediction to real-time speech recognition using TensorFlow Lite and Edge Impulse. Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP. Finally, you'll learn advanced techniques like on-device learning, deploying scikit-learn models, and power optimization.

This 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. These will help you stay up to date with the latest developments in the tinyML community.

Finally, take your tinyML solutions to the next level with microTVM, microNPU, and on-device learning. This book will give you the knowledge to make the most of your microcontroller and create unique projects with tinyML!

What You Will Learn:

• Understand the microcontroller programming fundamentals
• Work with real-world sensors, such as the microphone, camera, and accelerometer
• Run on-device ML with TensorFlow Lite for Microcontrollers
• Implement an app that responds to human voice with Edge Impulse
• Leverage transfer learning with FOMO and Keras
• Squeeze ML models into tight memory with quantization and other optimization methods
• Create gesture-recognition and music genre classifier apps with the Raspberry Pi Pico
• Design a CIFAR-10 model for memory-constrained 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.