Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • TinyML Cookbook
  • Toc
  • feedback
TinyML Cookbook

TinyML Cookbook

By : Gian Marco Iodice
4.9 (11)
close
TinyML Cookbook

TinyML Cookbook

4.9 (11)
By: Gian Marco Iodice

Overview of this book

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
Table of Contents (10 chapters)
close

Live classifications with the Arduino Nano

If you found live classification with the smartphone helpful, live classification with the Arduino Nano will be even more helpful.

This recipe will show how to pair the Arduino Nano with Edge Impulse to perform live classifications directly from our target platform.

Getting ready

Testing model performance with the sensor used in the final application is a good practice to have more confidence in the accuracy results. Thanks to Edge Impulse, it is possible to perform live classification on the Arduino Nano with a few simple steps that you can also find at the following link: https://docs.edgeimpulse.com/docs/arduino-nano-33-ble-sense.

How to do it…

Live classifications with the built-in microphone on the Arduino Nano require installing additional software on your machine. The different tools work on Linux, macOS, and Windows, and are listed here:

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete