For a convolutional layer $l$, the output dimensions and number of parameters are calculated as follows:

Input Size: $n_h^{[l-1]} imes n_w^{[l-1]} imes n_c^{[l-1]}$ Filter (Kernel) Size: $k_h^{[l]} imes k_w^{[l]} imes n_c^{[l-1]}$ *(Note: $n_c^{[l]}$ is the number of filters in layer $l$, matching the number of output channels.) Stride: $s_h^{[l]} imes s_w^{[l]}$ Padding: $p_h^{[l]} imes p_w^{[l]}$

Output (Activations $a^{[l]}$) Size: $n_h^{[l]} imes n_w^{[l]} imes n_c^{[l]}$