While training a deep neural network for a regression task, the model starts to memorize the training data. What's a suitable approach to address this issue?
- Increase the learning rate
- Add more layers to the network
- Apply dropout regularization
- Decrease the batch size
Memorization indicates overfitting. Applying dropout regularization (Option C) is a suitable approach to prevent overfitting in deep neural networks. Increasing the learning rate (Option A) can lead to convergence issues. Adding more layers (Option B) can worsen overfitting. Decreasing the batch size (Option D) may not directly address memorization.
Loading...
Related Quiz
- For time-series data, which variation of gradient boosting might be more appropriate?
- In time series data analysis, which method can be used to fill missing values by taking the average of nearby data points?
- Which metric provides a single score that balances the trade-off between precision and recall?
- _______ is a technique in ensemble methods where models are trained on different subsets of the data.
- Which technology is NOT typically associated with real-time data processing?