Create a computer vision system using decision tree algorithms to solve a real-world problem : Convolutions - Sharpening

The video tutorial introduces convolutions in image processing, highlighting their importance in feature extraction and manipulation. It explains the concept of kernel matrices, which are used to apply effects like blurring and sharpening to images. A practical example demonstrates how convolutions work by scanning an image with a kernel matrix. The tutorial also covers common kernels, such as sharpening and blurring, and their applications in real-world scenarios like Snapchat filters. The session concludes with a preview of further exploration in a Jupyter notebook.

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