Which technique is commonly used to prevent overfitting in neural networks?
Practice Questions
Q1
Which technique is commonly used to prevent overfitting in neural networks?
Increasing the learning rate
Using dropout
Reducing the number of layers
Applying batch normalization
Questions & Step-by-Step Solutions
Which technique is commonly used to prevent overfitting in neural networks?
Step 1: Understand what overfitting means. Overfitting happens when a model learns the training data too well, including noise and outliers, which makes it perform poorly on new data.
Step 2: Learn about dropout. Dropout is a technique used in training neural networks.
Step 3: Know how dropout works. During training, dropout randomly turns off (sets to zero) a certain percentage of neurons in the network.
Step 4: Realize the purpose of dropout. By turning off some neurons, the model learns to rely on different paths and features, which helps it generalize better to new data.
Step 5: Remember that dropout is applied only during training, not during testing or when making predictions.
