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In the context of neural networks, what does 'dropout' refer to?
Practice Questions
Q1
In the context of neural networks, what does 'dropout' refer to?
A method to reduce data size
A technique to prevent overfitting
A way to increase model complexity
A process for feature selection
Questions & Step-by-Step Solutions
In the context of neural networks, what does 'dropout' refer to?
Steps
Concepts
Step 1: Understand that neural networks are models that learn from data.
Step 2: Know that during training, these models can sometimes learn too much from the training data, which is called overfitting.
Step 3: Dropout is a technique used to help prevent overfitting.
Step 4: During training, dropout randomly turns off (sets to zero) a certain percentage of neurons in the network.
Step 5: By doing this, the model learns to rely on different neurons and not just a few, making it more robust.
Step 6: When the model is tested or used after training, all neurons are active, which helps it perform better on new data.
No concepts available.
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