Another optimization technique is using batch normalization, which normalizes the inputs to each layer, reducing the internal covariate shift and improving the overall stability and speed of training.

Learning rate scheduling is another useful optimization method for FFNNs. By adjusting the learning rate during training, either decreasing it gradually or using adaptive learning rate algorithms like Adam or RMSprop, we can find a balance between quick convergence and avoiding overshooting the optimal solution.

Employing early stopping is also a common technique in FFNN optimization. By monitoring the validation loss during training, we can stop the training early when the model starts overfitting on the training data, resulting in improved generalization capabilities.

One way to optimize feedforward neural networks is by using a technique called regularization, which helps prevent overfitting of the model to the training data. This can be achieved by adding a regularization term to the loss function, such as L1 or L2 regularization.