Intro to TinyML Part 1: Training a Neural Network for Arduino in TensorFlow | DigiKey

In this tutorial series, Shawn introduces the concept of Tiny Machine Learning (TinyML), which consists of running machine learning algorithms on microcontrollers.

For the first part, we use TensorFlow and Google Colab to train a simple neural network model that predicts the output of the sine function. While this is an inefficient method of creating a sinewave, it allows us to play with small, functioning, and non-linear neural networks.

The example training steps shown in this video are accomplished with Google Colab. This web-based Python editing software allows us to play with TensorFlow without needing to install various packages on our local machine.

Once we have a functioning model, we convert itto a TensorFlow Lite (tflite) model file. We then write a quick script that reads the bytes from the tflite file and creates a C header file for us to load into our embedded program on the next episode.

Finally, we can download both the .tflite and .h header file to our computer for deployment to the Arduino, which we will cover in the next episode. Netron can be used to examine the model in a slick GUI.