This interactive visualization demonstrates how various convolution parameters affect shapes and data dependencies between the input, weight and output matrices. Hovering over an input/output will highlight the corresponding output/input, while hovering over an weight will highlight which inputs were multiplied into that weight to compute an output. (Strictly speaking, the operation visualized here is a correlation, not a convolution, as a true convolution flips its weights before performing a correlation. However, most deep learning frameworks still call these convolutions, and in the end it's all the same to gradient descent.)