Deep learning has traditionally struggled with tabular data due to its heterogeneous nature, which includes diverse numerical and categorical data types. While deep learning excels in domains with homogeneous data like images and audio, its performance on tabular data has been inconsistent. Researchers such as Borisov et al. have highlighted the challenges posed by the statistical properties and weaker correlations in tabular datasets, compared to spatial or semantic data. Recent studies, including those by Kadra et al. and Shavit and Segal, suggest that regularization techniques can enhance deep learning's effectiveness on tabular data, even surpassing traditional models like gradient boosting. Kadra et al. propose "regularization cocktails," a combination of various regularization methods, tailored for specific datasets. Meanwhile, Shavit and Segal introduce the concept of Regularization Neural Networks, which utilize "Counterfactual Loss" for more efficient hyperparameter tuning. Despite these promising developments, the debate continues on whether improving deep learning for tabular data is as productive as advancing existing models like XGBoost. Further research is needed to address these questions and optimize deep learning's application in this domain.