cnn

Implementing conv2d proves you understand what a CNN layer actually computes, not just that you can name it. The signal is correct output-shape math, clean stride/padding handling, and knowing the im2col trick frameworks really use.

CNNs are foundational even as transformers rise, and this checks whether you understand why convolution suits images. The signal is parameter sharing and local connectivity, what pooling buys, and how the receptive field grows. Here is the answer.

Patches as tokens, global attention from layer one, and a weaker inductive bias than a CNN. The signal is naming the data regime where each architecture wins and why. Here is the answer interviewers score highest.