AI Dictionary
Dropout
Definition
A regularization technique for reducing overfitting in neural networks by preventing complex co-adaptations on training data.
Deep Dive
Dropout is a powerful regularization technique used in training neural networks, designed to prevent overfitting by making the model more robust to noisy or incomplete input data. During each training iteration, a random subset of neurons (along with their connections) in a specific layer is temporarily "dropped out" or ignored, effectively setting their output to zero. This means that these neurons do not contribute to the forward pass and are not updated during the backward pass for that particular training example. The probability of dropping a neuron is a hyperparameter, typically set between 0.2 and 0.5.
Examples & Use Cases
- 1Applying dropout to the hidden layers of a Convolutional Neural Network (CNN) to improve image classification accuracy
- 2Using dropout in a Recurrent Neural Network (RNN) to prevent overfitting on sequential data like text or time series
- 3Regularizing a large, fully connected neural network used for complex pattern recognition
Related Terms
RegularizationOverfittingNeural Networks