<ul><li>At the core of a CNN, convolution filters (also called kernels) play a crucial role in detecting patterns within an image.</li><li>Convolution filters slide over the image, performing element-wise multiplication and summation to extract features like edges, corners, and textures.</li><li>Multiple filters are applied in each convolutional layer, mimicking the progressive understanding of an image from basic to complex elements.</li><li>CNNs enable automatic learning of relevant patterns, eliminating the need for manual coding of feature detection.</li></ul>

Understanding Convolutional Neural Networks (CNNs): The Power of Convolution Filters

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