The dropout technique is a powerful tool used in neural networks to prevent overfitting during training. 🛡️ It works by randomly dropping out (setting to zero) a proportion of neurons in a layer during each forward pass. This prevents neurons from co-adapting too much to the training data, forcing the network to learn more robust features. Dropout essentially acts as a form of regularization, making the network more resilient to noise and ensuring it generalizes well to unseen data. 🚀

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