When working with Machine Learning projects on microcontrollers and embedded devices the dimension of features can become a limiting factor due to the lack of RAM: dimensionality reduction (eg. PCA) will help you shrink your models and even achieve higher prediction accuracy.
Support Vector Machines are very often used for classification tasks: but you may not know that they're so flexible they can be used for anomaly detection and novelty detection. Thanks to the micromlgen package, you can run One Class SVM on your Arduino microcontorller.
If you liked my post about ESP32 cam motion detection, you'll love this updated version: it's easier to use and blazing fast!
In earlier posts I showed you can run incremental binary classification on your microcontroller with Stochastic Gradient Descent or Passive-Aggressive classifier. Now it is time to upgrade your toolbelt with a new item: One-vs-One multiclass classifier.
Stochastic gradient descent is a well know algorithm to train classifiers in an incremental fashion: that is, as training samples become available. This saves you critical memory on tiny devices while still achieving top performance! Now you can use it on your microcontroller with ease.
When working with memory constrained devices you may not able to keep all the training data in memory: passive-aggressive classifiers may help solve your memory problems.
In the previous post we learnt it is possible to train a Machine learning classifier directly on a microcontroller. In this post we'll look into how to do it to classify colors.
In this hands-on guide about on-board SVM training we're going to see a classifier in action, training it on the Iris dataset and evaluating its performance.
As of now, we know it is possible to run Machine learning inference on tiny microcontrollers thanks to Tensorflow for Micro and my very own library MicroML. What if you could train a classifier directly on the microcontroller, too?
Ever wanted to use your thermal camera with Arduino but found it difficult to go beyond the tutorials code? Let's see the easiest possible way to view your thermal camera streaming without an LCD display!
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