Example Concept: To improve accuracy without overfitting, you can use techniques such as regularization (L1 or L2), dropout, and early stopping. Regularization adds a penalty to the loss function to discourage overly complex models. Dropout randomly sets a fraction of the neurons to zero during training, which helps prevent co-adaptation of neurons. Early stopping monitors the model's performance on a validation set and halts training when performance starts to degrade, preventing the model from learning noise in the training data.