Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and Raspberry Pi Pico

We are now ready for our final chapter of this practical learning journey into tinyML.

If you have made it this far, I bet you have a myriad of questions in mind to help you start or continue building compelling applications with machine learning (ML) on microcontrollers. Therefore, this chapter has a different format, seeking to answer three questions you might be pondering.

The first question will delve into the feasibility of training models directly on microcontrollers. How can we have on-device learning on microcontrollers? And what groundbreaking applications can this unlock? In this part, we will discuss the backpropagation algorithm to train a shallow neural network. We will also show how to use the CMSIS-DSP library to accelerate its implementation on any microcontroller with an Arm Cortex-M CPU.

After discussing on-device learning, we will tackle...