Residual connections are a crucial architectural feature in deep learning, particularly in neural networks like ResNets. They allow for the creation of shortcut paths that bypass one or more layers, enabling the network to learn identity functions more easily. This design helps mitigate issues such as vanishing gradients, which can hinder the training of very deep networks. By facilitating the flow of information and gradients, residual connections enhance the model’s ability to learn complex representations, leading to improved performance and accuracy in various tasks. Their innovative approach has made them a foundational element in modern deep learning architectures.