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2D convolution forward pass in NumPy, with parameter counts
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- Given the input tensor shape, input channels, output channels, kernel size, stride and padding, how many parameters does a Conv2D layer have, with and without bias? What is the output shape?
- Implement the forward pass of a 2D convolution in NumPy, first with plain loops.
- Vectorize it (for example with an im2col-style reshaping) and check it against your loop version.
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