Fighting Overfitting in Deep Learning - KDnuggets

While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can't get 100% accuracy. And even if we did, the result is still not without errors. There are simply too few test situations to find them. You may ask, what is the matter?