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CNN convolution kernels are also learnt

CNN convolution kernels are not hardcoded. They are learnt and optimized via backpropagation. You just specify their size! Example in PyTorch you'd do just: 
nn.Conv2d(1, 6, kernel_size=(5, 5))
as used for example at: activatedgeek/LeNet-5.
This can also be inferred from: stackoverflow.com/questions/55594969/how-to-visualise-filters-in-a-cnn-with-pytorch where we see that the kernels are not perfectly regular as you'd expected from something hand coded.

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