A residual network (ResNet) is a deep neural network architecture that uses skip connections — adding the input of a layer directly to its output — to enable training of very deep networks (hundreds or thousands of layers). Without skip connections, deep networks suffer from vanishing gradients where signals weaken through many layers. ResNets solved this problem and enabled the deep learning revolution in computer vision. They remain foundational for image classification, object detection, and feature extraction backbones.