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Convolutions are fundamental operations in deep learning, especially within Convolutional Neural Networks (CNNs). They work by applying a small matrix, known as a kernel (or filter), that slides across an input image to detect key features like edges, textures, or shapes. At each step, the kernel multiplies its values with the pixels underneath and sums them up, creating a feature map that emphasizes certain patterns. For instance, in the MNIST dataset, a 3×3 kernel can scan a 28×28 grayscale im
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