We had mentioned three main terminologies related to the CNN architecture:

- Convolutions
- Pooling
- Feature Maps

In this and the next few segments, we will go through each one of them in detail. Let’s start by understanding how **convolutions** work.

**Convolution**

Mathematically, the convolution operation is the **summation of the element-wise product** of two matrices. Let’s take two matrices, X and Y. If you ‘convolve the image X using the filter Y’, this operation will produce the matrix Z.

Finally, you compute the sum of all the elements in Z to get **a** **scalar number, **i.e. 3+4+0+6+0+0+0+45+2 = 60.

In the next segment, you will build on your newly acquired understanding of convolutions.

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