What is the main weakness of an artificial neural network?
One of the main weaknesses of artificial neural networks (ANNs) is their susceptibility to overfitting. Overfitting occurs when a neural network is trained too much on a specific dataset and becomes too complex, causing it to perform well on the training data but poorly on new, unseen data. This can happen when the network has too many layers, too many neurons in each layer, or too many training iterations.
Another weakness of ANNs is their lack of explainability. While ANNs can make accurate predictions, it can be difficult to understand how the network arrived at those predictions. This can be problematic when the decision-making process needs to be understood or explained, such as in medical diagnosis or legal cases.
Finally, ANNs require large amounts of labeled data to be trained effectively. This can be a significant limitation when working with small datasets or when acquiring labeled data is difficult or expensive.