  This article demonstrates that convolutional operation can be converted to
matrix multiplication, which has the same calculation way with fully connected
layer. The article is helpful for the beginners of the neural network to
understand how fully connected layer and the convolutional layer work in the
backend. To be concise and to make the article more readable, we only consider
the linear case. It can be extended to the non-linear case easily through
plugging in a non-linear encapsulation to the values like this $\sigma(x)$
denoted as $x^{\prime}$.
