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TinyML Cookbook

You're reading from   TinyML Cookbook Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801814973
Length 344 pages
Edition 1st Edition
Tools
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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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Toc

Table of Contents (10) Chapters Close

Preface 1. Chapter 1: Getting Started with TinyML 2. Chapter 2: Prototyping with Microcontrollers FREE CHAPTER 3. Chapter 3: Building a Weather Station with TensorFlow Lite for Microcontrollers 4. Chapter 4: Voice Controlling LEDs with Edge Impulse 5. Chapter 5: Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano 6. Chapter 6: Building a Gesture-Based Interface for YouTube Playback 7. Chapter 7: Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS 8. Chapter 8: Toward the Next TinyML Generation with microNPU 9. Other Books You May Enjoy

Importing weather data from WorldWeatherOnline

The effectiveness of ML algorithms depends heavily on the data used for training. Hence, as we commonly say, the ML model is only good as the dataset. The essential requirement for a good dataset is that the input data must represent the problem we want to solve. Considering our context, we know from physics that temperature and humidity affect snow formation.

Hence, in this recipe, we will show how to gather historical hourly temperature, humidity, and snowfall data to build a dataset for forecasting snow.

The following Colab file (see the Importing weather data from WorldWeatherOnline section in the following repository) contains the code referred to in this recipe:

  • preparing_model.ipynb:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter03/ColabNotebooks/preparing_model.ipynb

Getting ready

On the internet, there are various sources from which we can gather hourly weather data, but most of...

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