A deep TensorFlow network fits training data but generalizes poorly to new data. Which technique can reduce overfitting?

Dropout methods are a regularization technique that randomly sets a fraction of neuron activations to zero during training. This prevents complex co-adaptations on the training data, forcing the network to learn more robust features and improving generalization to unseen data. Threading and serialization are software engineering concepts unrelated to model overfitting. Dimensionality reduction can help reduce overfitting by simplifying the input features, but dropout directly addresses the complexity of the neural network itself, making it a more direct and often more effective solution for deep networks.