Some best practices for training deep learning models include using a reasonable amount of labeled data, preprocessing the data appropriately, selecting appropriate network architectures, careful parameter tuning, and monitoring the training process. Regularization and early stopping techniques can also help prevent overfitting. Experimentation and iterative refinement are key to optimizing model performance.