Layer parameters and structure

Convolutional layers allow us to preserve the original image dimensions, thereby improving our ability to learn features and reduce our computational load. They do this by utilizing something called a filter, which slides across the image, learning features by computing dot products. For example, a typical filter on the first layer of a CNN might have size 5 x 5 x 3 (namely, 5 pixels width and height, and 3 because images have a depth of three colors, RGB).

Mathematically, it's done as follows: 

Here, w represents our filter, but it also represents our learnable weights. We take a transpose of our input filter, ...

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