This document describes the mathematical operations of a convolutional layer in a neural network. It shows that a convolutional layer can be represented as a matrix multiplication between the input feature maps and the convolutional kernels. To perform this matrix multiplication, the input is first transformed using im2col to form a 2D matrix, where each column consists of a patch from the input. This matrix is then multiplied with the kernel matrix to produce the output feature maps.

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matrix multiplication (=conv. layer computation)
L
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kernel 2
kernel 3
kernel 1
kernel 4
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patch5
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patch2
patch3
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kernel 1 L
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kernel 4