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TinyML Cookbook

You're reading from   TinyML Cookbook Combine machine learning with microcontrollers to solve real-world problems

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837637362
Length 664 pages
Edition 2nd Edition
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Author (1):
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Gian Marco Iodice Gian Marco Iodice
Author Profile Icon Gian Marco Iodice
Gian Marco Iodice
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Table of Contents (16) Chapters Close

Preface 1. Getting Ready to Unlock ML on Microcontrollers FREE CHAPTER 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

Building a Weather Station with TensorFlow Lite for Microcontrollers

Nowadays, we can quickly obtain information about the weather thanks to the convenience of smartphones, laptops, and tablets connected to the internet. However, have you ever considered how you would forecast the weather in remote regions without internet access?

This chapter will teach us how to implement a simple weather station with machine learning (ML) to predict the occurrence of snowfall based on the temperature and humidity of the last three hours.

In this chapter, we will focus on dataset preparation and show how to acquire historical weather data from WorldWeatherOnline. After preparing the dataset, we will show how to train a neural network with TensorFlow and quantize the model to 8-bit with TensorFlow Lite. In the last part, we will deploy the model on the Arduino Nano and Raspberry Pi Pico with TensorFlow Lite for Microcontrollers to build an application capable...

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