________ is a technique where during training, random subsets of neurons are ignored, helping to make the model more robust.
- Dropout
- Regularization
- Batch Normalization
- Activation Function
Dropout is a regularization technique that involves randomly deactivating a fraction of neurons during training. This helps prevent overfitting, making the model more robust and less dependent on specific neurons.
Loading...
Related Quiz
- Why might one opt to use a Deep Q Network over traditional Q-learning for certain problems?
- While linear regression is concerned with estimating the mean of the dependent variable, logistic regression estimates the probability that the dependent variable belongs to a particular ________.
- When using transfer learning, what part of the pre-trained model is typically fine-tuned for the new task?
- In the k-NN algorithm, as the value of k increases, the decision boundary becomes __________.
- In a situation where you have both numerical and categorical data, which clustering method might pose challenges, and why?