An intriguing aspect is how they can approximate any function, thanks to their universal approximation capability, making them incredibly versatile in modeling complex patterns. However, they often face challenges such as overfitting, where they perform excellently on training data but fail to generalize to new, unseen data. Techniques like dropout and regularization help mitigate this issue, promoting better generalization by preventing excessive reliance on any particular subset of features during training.