convolutional neural networks onvolution operation let' start by looking at the convolution operation in one dimension given an input (tand kernel ( )the convolution operation is given by  * an equivalent form of this operationgiven the commutativity of the convolution operationis as followss  * furthermorethe negative sign (flippingcan be replaced to get cross-correlationas followss  * deep learning literature and software implementations use the terms convolution and cross-correlation interchangeably the essence of the operation is that the kernel is much shorter set of data points as compared to the inputand the output of the convolution operation is higher when the input is similar to the kernel figure - and figure - illustrate this key idea we take an arbitrary input and an arbitrary kerneland perform the convolution operation the highest value is achieved when the kernel is similar to particular portion of the input