CNN Part 3 | Convolution Operation
Channel: CampusX
Date: 2022-08-23

Convolution is one of the main building blocks of a CNN. The term convolution refers to the mathematical combination of two functions to produce a third function. It merges two sets of information.

In the case of a CNN, the convolution is performed on the input data with the use of a filter or kernel (these terms are used interchangeably) to then produce a feature map.

We execute a convolution by sliding the filter over the input. At every location, matrix multiplication is performed and sums the result onto the feature map.

Digital Notes for Deep Learning: https://shorturl.at/NGtXg

Demo used in the video - https://deeplizard.com/resource/pavq7noze2

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⌚Time Stamps⌚

00:00 - Intro
00:39 - Recap
03:45 - Basics of Images
07:33 - Edge Detection
15:20 - 17:40 - Filters in CNN are managed by back propagation😊
22:30 - 26:45 - Shape of the filter output(Grey scale and RGB)
26:50 - 28:39 - Multiple filters
28:40 - Outro