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Convolution is an operation where a filter (a small matrix) is applied to some input (typically a much larger matrix).

Convolution is a common operation in image processing, but convolution has also found some applications in natural language processing, audio processing, and other fields of machine learning.


Padding is a preprocessing step before a convolution operation. The input matrix is often padded to control the output dimensions of the convolution, or ot preserve information around the edges of the input matrix.


Stride is the number of steps the filter takes in the convolution operation.

Calculating the output dimensions of a convolution

For example, let’s say we have \(n \times n\) matrix \(A\) and a \(f \times f\) filter \(F\). The output dimension depends on two parameters – padding \(p\) and stride \(s\).

The dimensions for the output matrix \(A * F\) will be

\[ \left \lfloor \frac{n + 2p - f}{s} \right \rfloor + 1 \times \left \lfloor \frac{n + 2p - f}{s} \right \rfloor + 1 \].

In a same convolution, \(s = 1\) and \(p = \frac{f - 1}{2}\). The \(n \times n\) matrix \(A\) gets padded to $ n + p n + p$ and the output matrix becomes \(n \times n\).