In Convolutional neural network, the kernel is nothing but a filter that is used to extract the features from the images. The kernel is a matrix that moves over the input data, performs the dot product with the sub-region of input data, and gets the output as the matrix of dot products. Kernel moves on the input data by the stride value. If the stride value is 2, then kernel moves by 2 columns of pixels in the input matrix. In short, the kernel is used to extract high-level features like edges from the image.
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You can watch this video on How CNN works to understand more about kernel: