Overfitting occurs when a neural network learns the details and noise in the training data to the extent that it negatively impacts the performance of the model on new, unseen data. This happens when the model becomes too complex and starts memorizing the training examples rather than generalizing from them.
Overfitting can be avoided by using techniques such as regularization (e.g., L1/L2), dropout, and data augmentation. Additionally, early stopping and using simpler models can help prevent overfitting by ensuring the model does not learn irrelevant details.
Ensuring a proper balance between model complexity and available data is key to achieving generalization and avoiding overfitting.