You have a dataset that is split into training gcp video

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You have a dataset that is split into training, validation, and test sets. All the sets have similar distributions. You have sub-selected the most relevant features and trained a neural network in TensorFlow. TensorBoard plots show the training loss oscillating around 0.9, with the validation loss higher than the training loss by 0.3. You want to update the training regime to maximize the convergence of both losses and reduce overfitting. What should you do?