Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Dropout is a regularization technique specifically designed to prevent overfitting in neural networks. Overfitting occurs when a model learns not just the underlying patterns in the training data, but also the noise and outliers, making it less effective when applied to new, unseen data.

The dropout technique works by randomly 'dropping out' a fraction of the neurons (and their connections) during training, which means that during each training iteration, only a subset of the neurons is active or contributes to the forward and backward passes. This randomness forces the network to learn more robust features that are less dependent on specific neurons, thereby improving its ability to generalize to new data.

The method helps to ensure that the model does not rely too heavily on any individual neuron, thus promoting redundancy and reducing the chance that the model will memorize the training data rather than learn the underlying structure. Consequently, dropout can significantly enhance the performance of neural networks on unseen data, achieving a better balance between bias and variance.