Example Concept: To improve accuracy without overfitting, you can use regularization techniques like L1 or L2 regularization, which add a penalty to the loss function to discourage overly complex models. Additionally, dropout can be applied during training to randomly deactivate neurons, preventing the network from becoming too reliant on specific nodes. Data augmentation artificially increases the diversity of the training dataset by applying transformations such as rotation, scaling, and flipping, which helps the model generalize better to unseen data. Finally, using early stopping during training can halt the process when the model's performance on a validation set starts to degrade, indicating potential overfitting.