This article introduces Delta-Delta Learning (DDL), which enhances standard residual networks by applying a rank-1 transformation to the hidden state matrix. The Delta-Res block update combines the removal of old information with the addition of new data, controlled by a gate. Key components include a reflection direction, a value vector, and a gate parameter.