Convolution Operation Syllabus: Purpose of Convolution How Convolution Works Convolution Layers Filter Basics Weight Initialization Learned Filters Multiple Filters (1) Purpose Of Convolution The purpose of convolution in the context of image processing, computer vision, and convolutional neural networks (CNNs) is to extract and process features from input data efficiently. It enables the model to understand patterns, spatial hierarchies, and key attributes in the data, such as edges, textures, shapes, and objects. (2) How Convolution Works? Convolution is a fundamental operation in Convolutional Neural Networks (CNNs), used primarily for feature extraction. It allows the network to automatically learn spatial hierarchies

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