Following our look at weight regularization techniques like L1 and L2, which penalize large weight values, we now turn to a distinctly different method for combating overfitting: Dropout. This technique works by randomly setting a fraction of neuron outputs to zero during each training update. This prevents complex co-adaptations where neurons become overly reliant on specific other neurons.

This chapter explains the mechanism behind Dropout, including how it operates during training and how activations are adjusted during inference (test time). We will cover the common 'inverted dropout' implementation, discuss the dropout rate $p$ as a tunable hyperparameter, and touch upon considerations for applying Dropout in convolutional and recurrent networks. Finally, you'll see how to integrate Dropout layers into models using standard deep learning frameworks through practical examples.