Example Concept: To prevent overfitting in neural networks, you can use regularization techniques such as L1 or L2 regularization, which add a penalty to the loss function based on the size of the weights. Dropout is another effective method, where random neurons are ignored during training, forcing the network to learn more robust features. Additionally, data augmentation artificially expands the training dataset by applying transformations like rotation, scaling, and flipping to the input data, helping the model generalize better to unseen data.