Choose the general limitations of the backpropagation rule among the following.

The general limitations of the backpropagation rule include:

1. Local Minima: Backpropagation can get trapped in local minima, leading to suboptimal solutions rather than finding the global minimum of the loss function.

2. Vanishing and Exploding Gradients: In deep networks, gradients can become very small (vanishing) or very large (exploding), which can hinder learning or cause instability.

3. Overfitting: Backpropagation may lead to overfitting, particularly with small datasets and complex models, as it attempts to minimize training error instead of generalizing well on unseen data.

4. Computationally Intensive: Training deep networks using backpropagation can be extremely resource-intensive regarding time and memory, particularly for very large datasets.

These limitations can be addressed through various techniques, such as using optimizers like Adam or applying methods like dropout for regularization.