Decide if and how to apply deep learning to tabular data, with PyTorch architecture, training loop, and regularization.
## CONTEXT Deep learning conquered images and text, but on tabular data gradient boosting often still wins, so the first job is deciding whether a neural net is even justified. In 2026, architectures like TabNet, FT-Transformer, and well-regularized MLPs with embeddings can match or exceed boosting on large,…
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