Question: Solve the following by considering a convolution layer with 1 0 0 feature maps each with 3 x 3 kernel, a stride of 2 and

Solve the following by considering a convolution layer with 100 feature maps each with 3 x 3 kernel, a stride of 2 and "same" padding. The input is a 200 x 300 RGB image.
What is the total number of parameters in the layer?
What would be the total number of float multiplication?
How much memory (RAM) the layer would occupy just for one instance considering feature maps are represented using 32-bit floats?
Also, examine why memory requirement during inference would be less as compared to the total memory requirement by all layers during model training.

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