This is how I see it: Statistical modeling: input -> feature extraction (manual) -> model selection (manual) -> output Machine learning: input -> feature extr

This is how I see it: Statistical modeling: input -> feature extraction (manual) -> model selection (manual) -> output Machine learning: input -> feature extraction (manual) -> model selection (auto) -> output Deep learning: input -> feature extraction (auto) -> model selection (auto) -> output So take a DL image classifier. The convolution + pooling layers perform automatic feature extraction. Back to OPs point, why use something like DL when you've already engineered your features?