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.
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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.
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!