A manufacturing company has a production line with sensors that collect hundreds of quality metrics.

A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1. ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.

Convolutional Neural Networks (CNNs / ConvNets)

PyTorch ResNet: The Basics and a Quick Tutorial