What is the role of dropout in neural networks?

What is the role of dropout in neural networks?

Step 1: Understand that neural networks learn from data to make predictions.
Step 2: Know that sometimes, neural networks can learn too much from the training data, which is called overfitting.
Step 3: Overfitting means the model performs well on training data but poorly on new, unseen data.
Step 4: Dropout is a technique used during training to help prevent overfitting.
Step 5: During training, dropout randomly turns off (sets to zero) a certain percentage of neurons in the network.
Step 6: By turning off some neurons, the network learns to rely on different paths and features, making it more robust.
Step 7: This helps the model generalize better to new data, improving its performance.

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