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In some iterative learning methods the more iterations you apply the more specific your model becomes about the training set. If there are too many iterations, your model will become too specifically trained for the training samples and will score less on other samples that are not seen during the training phase. This is call over-fitting, though over-...


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Early stopping: stop the training when a condition is met Checkpoint : frequently save the model The purpose of Early Stopping is to avoid overfitting by stopping the model before it happens using a defined condition. If you use it, and then you save the model when the training is stopped*, you will get a model that is assumed to be good enough and not ...


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Early stopping and callbacks are two different concepts: Early stopping is a machine learning concept about when to stop training your model to avoid overfitting: You monitor a target value (e.g. validation loss) and stop learning after it hits a minimum. If the monitored value keeps increasing for a couple of epoch, you can restore the weights from the ...


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Running for "to many" epochs can indeed lead to over fitting. You should look at the validation loss. If on AVERAGE it continues to decrease then you are not yet over fitting. You may be tempted to run more epochs in hopes your loss will decrease but unless you adjust your learning rate dynamically at some point you won't get any improvement. If you use ...


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Training a neural network for "too many" epochs than needed without using early stopping criterion leads to overfitting, where your model's ability to generalize decreases.


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Okay, I think it's better if we distinguish loss and accuracy first via Jeremy's answer, and I agree with him with the sentence "low or huge loss is a subjective metric". The loss value is easy to affect by noise from data and significant increase with a few error data points. My advice in this case is to use more evaluation metrics, and understand ...


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