You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during…

The "overfit a small batch" or "sanity check on a tiny dataset" technique is a fundamental first debugging step for deep learning models when training loss is not decreasing. If the model cannot achieve a very low (near zero) loss on a small, representative subset of the data, it indicates a fundamental issue with the model architecture, data preprocessing, loss function, or optimizer setup, suggesting it's unable to learn anything at all.

Adding handcrafted features is a strategy to improve model performance or interpretability but is not a primary debugging step for a model that isn't learning anything at all; it doesn't address the fundamental issue of unchanging loss.

Using hyperparameter tuning is premature when the model isn't learning at all (losses aren't changing); tuning a learning rate is only useful if the model shows some sign of learning initially.

Training for more epochs or using hardware accelerators is about scaling or prolonging training, not debugging a model that shows no signs of learning from the outset; if the loss isn't changing, more training won't fix a fundamental issue.