Dropout
Dropout is a regularization technique used in neural networks to prevent overfitting by randomly dropping units (neurons) and their connections during training. It works by temporarily removing a random subset of neurons from the network at each training step, forcing the remaining neurons to learn more robust features. This helps improve generalization and reduces the model's reliance on specific neurons, making it more resilient to noise in the data.
Developers should learn and use Dropout when building deep learning models, especially in scenarios with limited training data or complex architectures prone to overfitting, such as large convolutional neural networks (CNNs) or recurrent neural networks (RNNs). It is particularly useful in computer vision, natural language processing, and other domains where models need to generalize well to unseen data, as it enhances performance on validation and test sets without requiring additional data.