Hands-On Image Processing with Python

Dropout

Dropout is the most popular regularization technique for deep neural networks. Dropout is used to prevent overfitting, and it is typically used to increase the performance (accuracy) of the deep learning task on the unseen dataset. During training time, at each iteration, a neuron is temporarily dropped or disabled with some probability, p. This means all the input and output to this neuron will be disabled at the current iteration. This hyperparameter p is called the dropout rate, and it's typically a number around 0.5, corresponding to 50% of the neurons being dropped out.