Dr. James McCaffrey of Microsoft Research details the 'Hello World' of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset.

The table includes neural-network parameters, input resolution, and the associated processing demands for four example models for informational and comparison purposes.

Although machine learning is becoming more popular in different fields of Earth Sciences, some concepts of convolutional neural networks may be vaguely understood by non-practitioners.

This is called the latent space of the AI model. In general, the lower layers of a multilayered convolutional neural network will learn basic features such as corners and edges.

Description Convolutional Neural Networks (CNN) are mainly used for image recognition. The fact that the input is assumed to be an image enables an architecture to be created such that certain ...

Concluding this series on convolutional neural networks (CNNs), we explain the hardware conversion of a CNN and specifically the benefits of using an AI microcontroller with a hardware-based CNN ...