![]() ![]() The best option is to get more training data. ![]() There are several manners in which we can reduce overfitting in deep learning models. The training metric continues to improve because the model seeks to find the best fit for the training data. Usually, the validation metric stops improving after a certain number of epochs and begins to decrease afterward. We can identify overfitting by looking at validation metrics like loss or accuracy. In other words, the model learned patterns specific to the training data, which are irrelevant in other data. ![]() Overfitting occurs when you achieve a good fit of your model on the training data, but it does not generalize well on new, unseen data. ![]()
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