convolutions are important building blocks of state-of-the-art models because they can be used for different goals.
One goal is the use them as a dimensionality reduction technique. Let's understand this by going through an example.
If the convolution operation is applied to an input volume of 
and it is convolved with a set of 
filters, each one being in size, the number of features is reduced from 512 to . The output ...
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