Chapter 5, Convolutional Neural Networks, covered the theory behind CNNs, and convolution of course has been part of that presentation. Let's do a recap of this concept from a mathematical and practical perspective before moving on to object recognition. In mathematics, convolution is an operation on two functions that produces a third function, which is the result of the integral of the product between the first two, one of which is flipped:

Convolution is heavily used in 2D image processing and signal filtering.

To better understand what happens behind the scenes, here's a simple Python code example of 1D convolution with NumPy ...

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