Dropout
Regularization technique that randomly sets some neurons to zero during training to prevent overfitting and improve generalization.
Key Concepts
Regularization
A technique that is used to prevent overfitting.
Overfitting
A phenomenon that occurs when a model fits too closely to the training data, memorizing noise rather than general patterns.
Generalization
The ability of a model to perform well on unseen data.
Detailed Explanation
Dropout is a regularization technique that is used to prevent overfitting in neural networks. It works by randomly setting some neurons to zero during training. This forces the network to learn more robust features, as it cannot rely on any single neuron.
Dropout is a very effective technique for preventing overfitting, and it is used in a wide variety of deep learning models.
Real-World Examples & Use Cases
Image Recognition
Dropout is used to prevent overfitting in image recognition models.
Natural Language Processing
Dropout is used to prevent overfitting in natural language processing models.
Machine Translation
Dropout is used to prevent overfitting in machine translation models.