CNN Part 3 | Convolution Operation

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 ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! 💭Share your thoughts, experiences, or questions in the comments below. I love hearing from you! ⌚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