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How to solve the overfitting problems in deep learning
Overfitting occurs when you achieve a good fit of your model on the training data, while it does not generalize well on new, unseen data. In other words, the model learned patterns specific to the training data, which are irrelevant in other data. We can identify overfitting by looking at validation metrics, like loss or accuracy. Usually, the validation metric stops improving after a certain number of epochs and begins to decrease afterward. The training metric continues to improve because the model seeks to find the best fit for the training data. There are several manners in which we can reduce overfitting in deep learning models. The best option is to get more training data. Unfortunately, in real-world situations, you often do not have this possibility due to time, budget or technical constraints. Another way to reduce overfitting is to lower the capacity of the model to memorize the training data. As such, the model will need to focus on the relevant patterns in training data, which results in better generalization. In following children nodes, we’ll discuss three options to achieve this.
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