Machine learning has come to the 'edge' - small microcontrollers that can run a very miniature version of TensorFlow Lite to do ML computations.

Learn how to perform machine learning model training on a computer and then run the created inference on a 32-bit processor.

We show how to configure TensorFlow with Keras on a PC and build a simple linear regression model use a NVIDIA GPU to take advantage of parallel processing.

In this tutorial, we will introduce the concept of Mel Frequency Cepstral Coefficients (MFCC) and how to compute them using Python libraries.

In this tutorial, we will briefly go over how a convolutional neural network (CNN) works and how to train one using TensorFlow and Keras.

This guide goes through how to train micro speech models on your own.

In this tutorial we will develop a Python program that reads the TensorFlow Lite model file and uses it to perform wake word recognition in real time.

n this tutorial, we’ll walk through installing TensorFlow Lite and using it to perform object detection with a pre-trained Single Shot MultiBox Detector model.

In this tutorial, we will load our model in Arduino using the TensorFlow Lite library and use it to run inference to generate an approximation of a sinewave.

We will create a neural network that is capable of predicting the output of the sine function, convert this model to TensorFlow Lite and examine it using Netron

Train a robust machine learning model and deploy to an ESP32 dev board using the Arduino TensorFlow Lite library to perform inference.