Dynamical Systems
Deep Learning
Technique to prevent overfitting by randomly deactivating neurons during training
Detailed Explanation
Dropout Regularization is a technique in deep learning that reduces overfitting by temporarily deactivating a random subset of neurons during each training iteration. This prevents neurons from becoming overly dependent on each other, promoting model robustness and improved generalization to unseen data. During inference, all neurons are active, leveraging the full network's capacity for predictions.
Use Cases
- •Use case: Enhances model generalization by preventing overfitting in neural networks for image classification tasks.
Related Terms
Other terms in the Deep Learning category