Why is dropout effective in deep networks?

The problem with deep neural networks is that training data with few examples are likely to be overloaded. Ensembling networks can reduce overfitting with different model configurations, but this requires additional effort to maintain multiple models and is computationally expensive. Dropout is one of the easiest and most successful ways to reduce dependencies and overcome overfitting problems in deep neural networks. Using the dropout regularization method, we use a single neural network model to resemble different network architectures by dropping nodes during training. It is considered an effective regularization method because it improves the generalization error and has a low computational cost.