Question: Complete the below table by writing the output dimension and the number of parameters at each layer. For all Convolutional layers, assume padding = True

Complete the below table by writing the output dimension and the number of parameters at each layer.
For all Convolutional layers, assume padding = True and stride 1. For Maxpool, assume padding = False and stride =2.
\table[[Layer,Output Dimension,Number of Parameters],[Input,,],[Conv (3\times 3\times 8),,],[ReLU,,],[Maxpool,,],[Batchnorm 2D,,],[Conv (3\times 3\times 16),,],[ReLU,,],[Maxpool,,],[Flatten,,],[Fully Connected -10,,]]
Which layer has the maximum number of learnable parameters?
Assume that we have a Batchnorm layer immediately after a Convolutional layer. Now, if we remove the bias parameter in the Convolutional layer, what effect will it have on the overall learning?
Complete the below table by writing the output

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