Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of input units to zero during training.
What is the role of dropout in a CNN?
Practice Questions
Q1
What is the role of dropout in a CNN?
To increase the number of neurons
To prevent overfitting
To enhance feature extraction
To speed up training
Questions & Step-by-Step Solutions
What is the role of dropout in a CNN?
Step 1: Understand what a CNN is. A CNN (Convolutional Neural Network) is a type of neural network used for image processing and recognition.
Step 2: Learn about overfitting. Overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data.
Step 3: Know what dropout does. Dropout is a technique that randomly turns off (sets to zero) a certain percentage of neurons during training.
Step 4: Realize the purpose of dropout. By turning off some neurons, dropout helps the model to not rely too much on any single neuron, which reduces overfitting.
Step 5: Remember that dropout is only used during training. When the model is being tested or used for predictions, all neurons are active.
No concepts available.
Soulshift Feedback×
On a scale of 0–10, how likely are you to recommend
The Soulshift Academy?
