Dropout Technique: The dropout technique is one of the popular techniques to avoid overfitting in the neural network models. It is the regularization technique, in which the randomly selected neurons are dropped during training.
One technique to avoid overfitting in neural networks is dropout.
Dropout is a regularization technique used during the training of neural networks to prevent overfitting. It works by randomly setting a fraction of the input units to zero at each update during training time, which helps to prevent complex co-adaptations on training data. This forces the network to learn more robust features and prevents it from relying too much on any particular set of features. As a result, dropout can help improve the generalization ability of the neural network and reduce overfitting.