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TinyML Cookbook

TinyML Cookbook

By : Gian Marco Iodice
4.9 (11)
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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)
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Live classifications with a smartphone

When we talk of model testing, we usually refer to the evaluation of the trained model on the testing dataset. However, model testing in Edge Impulse is more than that.

In this recipe, we will learn how to test model performance on the test set and show a way to perform live classifications with a smartphone.

Getting ready

Before implementing this recipe, the only thing we need to know is how we can evaluate model performance in Edge Impulse.

In Edge Impulse, we can evaluate the trained model in two ways:

  • Model testing: We assess the accuracy using the test dataset. The test dataset provides an unbiased evaluation of model effectiveness because the samples are not used directly or indirectly during training.
  • Live classification: This is a unique feature of Edge Impulse whereby we can record new samples either from a smartphone or a supported device (for example, the Arduino Nano).

The live classification approach...

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