Cross$-$Validation: Use cross$-$validation $($like k$-$fold cross$-$validation$)$ to ensure your model generalizes well to unseen data. Regularization: L$1$ and L$2$ Regularization: Add regularization terms to the loss function to penalize large weights. L$1$ regularization encourages sparsity, while L$2$ regularization discourages large weights. Dropout: Randomly drop neurons during training to prevent the model from becoming too reliant on specific neurons. Early Stopping: Monitor the model$$s performance on a validation set and stop training when performance on the validation set starts to degrade, indicating potential over$-$fitting. Data Augmentation: Apply transformations to the training data to generate more diverse examples, helping the model generalize better. Reduce