it refers to how often the network does not perform as expected
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One of the most common problems that I encountered while training deep neural networks is overfitting. Overfitting occurs when a model tries to predict a trend in data that is too noisy. This is the caused due to an overly complex model with too many parameters
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Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.please mark me brainlist
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