Parameters: these are the coefficients of the model, and they are chosen by the model itself. It means that the algorithm while learning, optimizes these coefficients (according to a given optimization strategy) and returns an array of parameters that minimize the error.

In a CNN, each layer has two kinds of parameters : **weights and biases**. The total number of parameters is just the sum of all weights and biases.

Wc= Number of weights of the Conv Layer.

Bc= Number of biases of the Conv Layer.

Pc=Number of parameters of the Conv Layer.

K = Size (width) of kernels used in the Conv Layer.

N = Number of kernels.

C = Number of channels of the input image.

**Wc = K^2 x C x N****Bc = N****Pc = Wc+Bc**

In a Conv Layer, the depth of every kernel is always equal to the number of channels in the input image. So every kernel has K^2xC parameters, and there are such kernels. Thatâ€™s how we come up with the above formula.