In this first tutorial from the series Arduino Machine learning we're going to implement the "Hello world" of Machine learning projects: classifying the Iris dataset on an Arduino board. The Iris dataset is a well known one in the Machine learning world and is often used in introductory tutorials about classification.
In this tutorial we're going to run the classification directly on a Arduino Nano board (old generation), equipped with 32 kb of flash and only 2 kb of RAM: that's the only thing you will need!
Tagmicroml
Are you getting started with Machine learning on Arduino boards? Do you want to run the model you trained in Python into any C++ project, be it Arduino, STM32, ESP32?
In this tutorial I'll show you how easy it is: we'll go from start to end in just 4 easy steps!
A lot of forum threads ask about the possibility to run Machine learning on Arduino.
The answers mostly follow in one of these 3 categories:
- Arduino is too resource-constrained to handle Machine learning
- Come up with a naive implementation of a Multi Layer Perceptron
- (recently) Sure! You can use Tensorflow Lite for Microcontrollers
No single answer I read talked about the other 100s alghoritms that fall under the Machine learning umbrella. No. Single. One. Let me explain what I think is wrong with this.
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