residual layers

Residual layers, also known as skip connections, are a type of neural network layer that allow for the flow of information between non-adjacent layers. In a traditional neural network, information flows through each layer in a linear fashion, with each layer transforming the input data in some way. In contrast, a residual layer allows the input data to bypass one or more layers and be directly added to the output of a later layer, creating a shortcut or "skip" connection. This helps to address the problem of vanishing gradients, where the gradients become too small to effectively update the weights in earlier layers. Residual layers have been shown to improve the performance of deep neural networks, particularly in image recognition and natural language processing tasks.