Answer
A common mistake with Backpropagation is applying it without fully understanding the underlying data or problem, which often leads to weak or misleading results. Skipping proper testing before relying on it in a real project is another frequent error.
Real-world example When a neural network mislabels a cat photo as a dog, backpropagation adjusts the internal weights so the next prediction is more likely to be correct.