#004 CNN Padding Master Data Science

In order to build deep neural networks, one modification to the basic convolutional operation that we have to use is padding. Let's see how it works. What we saw in earlier posts is that if we take a \(6 \times 6 \) image and convolve it with a \(3 \times 3 \) filter, we end up with a \(4 \times 4 \) output (or with a \(4 \times 4 \) matrix), and that's because the number of possible positions for our \(3 \times 3 \) filter, to fit in our \(6 \times 6 \) matrix is \(4 \times 4 \) . If we convolve an \(n\times n\) image with an \(f\times f\) filter, what are the dimensions of the output matrix? If we have an \(n\times n\), and convolve that with a \(f\times f \), then the dimension of the output will be: \((n-f 1)\times (n-f 1) \) .