What is the purpose of dropout in neural networks?

What is the purpose of dropout in neural networks?

Step 1: Understand that neural networks learn from data to make predictions.
Step 2: Know that sometimes, a neural network 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 'drops' or ignores a certain percentage of neurons (units) in the network.
Step 6: By dropping these units, 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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