TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems, 2nd Edition

December 7, 2023 Books
TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems, 2nd Edition

English | 2023 | ISBN: 978-1837637362 | 664 Pages | PDF, EPUB | 52 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

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

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
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