Tag Archives: generalization
Regularization Techniques to Improve Model Generalization
Introduction In our last discussion, we explored dropout regularization techniques, which involve randomly setting a fraction of the activations to zero during training. This helps prevent overfitting by encouraging the network to learn redundant representations and improving generalization. Today, we will extend our focus to other regularization methods, including L1 and L2 regularization, label smoothing,…
Dropout Regularization
Dropout How does the mask impact memory during training? While the masks used in dropout regularization introduce some additional memory overhead during training, this impact is generally modest compared to the overall memory usage of the neural network model. The benefits of improved generalization and reduced overfitting often outweigh the minor increase in memory usage….