Dropout

Dropout is a regularization technique used in neural networks to prevent overfitting. During training, it randomly sets a fraction of the neurons to zero, effectively 'dropping them out' of the network for that iteration. This helps the model to learn more robust features by not relying on any single neuron too much. Dropout is commonly used in deep learning applications, particularly in convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By introducing randomness, it encourages the network to develop multiple independent representations of the data, improving generalization to unseen data.