You're reading from TinyML Cookbook - Second Edition

Product type Book

Published in Nov 2023

Publisher Packt

ISBN-13 9781837637362

Pages 664 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Gian Marco Iodice

Table of Contents (16) Chapters

Preface

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

10. Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU

11. Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM

12. Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and Raspberry Pi Pico

13. Conclusion

14. Other Books You May Enjoy

15. Index

Preparing the dataset

The dataset preparation is a crucial phase in any ML project because it has implications for the effectiveness of the resulting trained model.

In this recipe, we will create a dataset from the physical quantities acquired earlier by adopting two techniques to make it suitable for training an accurate model. These two techniques will allow us to obtain a balanced dataset and bring its values into the same numerical range.

Getting ready

The dataset required for our task must be prepared to train a binary classification model, meaning the output can only belong to two classes: Yes, it snows or No, it does not snow. Therefore, mapping the snowfall in cm into these two classes is the first thing we must do. In this project, we have considered the snow formation only when the snowfall in cm is above 5 cm.

The next step for building the dataset concerns the selection of input features to predict the snow. Considering our goal of forecasting...