Tabular data has long been dominated by gradient boosting (XGBoost, LightGBM). When do neural networks win? 1) Very large datasets (>1M rows) where transformers excel. 2) Tasks with meaningful feature interactions that tree-based methods struggle to learn. 3) Multi-modal inputs (tabular + text/image). 4) Transfer learning scenarios where you have related tabular datasets. TabNet: sequential attention mechanism selects relevant features at each decision step. Interpretable: provides feature importance per instance. Implementation: pip install pytorch-tabnet; TabNetClassifier(n_d=64, n_a=64, n_steps=5, gamma=1.5); model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)]). FT-Transformer (Feature Tokenizer + Transformer): embeds each feature as a token, applies multi-head attention across features. Often best quality on medium datasets. SAINT (Self-Attention and Intersample Attention Transformer): attention across both features AND samples in a batch - captures inter-sample relationships. AutoML approaches: AutoGluon and H2O AutoML run multiple algorithms including neural networks and ensemble, often competitive with manual tuning. Practical recommendation: always benchmark XGBoost/LightGBM first (faster to train, less tuning). Use neural approaches when dataset >500K rows, or when initial neural experiments show promise. Hyperparameter search with Optuna reduces manual tuning burden.