What are the limitations of MLP?

The Hidden Hurdles of Multilayer Perceptrons

While multilayer perceptrons (MLPs) are a cornerstone of deep learning and capable of modeling complex patterns, they’re not without their flaws. In practice, several limitations can make working with MLPs more challenging than they first appear.

One major issue is their sensitivity to input scale. If features aren't properly normalized or standardized, MLPs can struggle to converge during training. This means even a well-designed network might fail simply because the input data wasn't prepped correctly. It's a subtle but critical detail that often trips up beginners.

Another significant drawback is their propensity for overfitting, especially when dealing with limited data. With many parameters to tune, MLPs can memorize training examples rather than learning generalizable patterns. Regularization techniques like dropout or L2 penalties help, but they add complexity and require careful tweaking.

And speaking of tuning—parameter sensitivity is a real headache. Choosing the right number of layers, neurons per layer, learning rate, and activation functions isn’t straightforward. Poor choices can lead to slow convergence or subpar performance, and finding the sweet spot often feels more like an art than a science.

Finally, training large MLPs on big datasets can be computationally expensive. Without substantial computing power—like GPUs or TPUs—training times can stretch from minutes to days. This makes experimentation slow and costly, particularly in resource-constrained environments.

Despite these challenges, MLPs remain widely used, especially in structured data tasks. But understanding their limitations helps practitioners apply them more effectively—and know when it’s better to choose a different tool altogether.