Which of the following techniques is commonly used to prevent overfitting in neu

₹0.0

Question: Which of the following techniques is commonly used to prevent overfitting in neural networks?

Options:

Correct Answer: Using dropout

Solution:

Dropout is a regularization technique that randomly sets a fraction of the neurons to zero during training to prevent overfitting.

Which of the following techniques is commonly used to prevent overfitting in neural networks?

Which of the following techniques 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 sets a certain percentage of neurons (units) to zero. This means those neurons do not contribute to the output for that training step.
Step 4: Understand the purpose of dropout. By randomly turning off neurons, dropout helps the model to not rely too much on any single neuron, which reduces the risk of overfitting.
Step 5: Remember that dropout is applied only during training, not during testing or validation.

No concepts available.