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A validation set is essential in training neural networks because it helps assess the model’s performance during training without using the test set. By evaluating the model on a separate validation set, we can monitor for overfitting, ensuring that the model generalizes well to unseen data. It allows for tuning hyperparameters, such as learning rates and configurations, and selecting the best model iteration. Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deepl
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