Executing operations element-by-element introduces substantial overhead beyond mere computational inefficiency. Every time a command is executed in Python, the interpreter must send it to the deep learning framework's engine, which then inserts it into the computational graph and schedules it for execution. This interpretative and scheduling overhead can be highly detrimental to performance, making it highly advisable to rely on vectorized operations and matrices whenever possible.