You could argue that deep learning is not a statistical method in the traditional sense, in that a typical neural network model is not a probability model, and some neural networks are well known to produce specifically bad probability models, requiring some amount of post processing in order to produce correctly "calibrated" probability predictions.
However I don't like that there is often a strict dichotomy presented between "deep learning" and "statistics". There is a whole world of gray areas and hybrid techniques, which tend to be both more accessible, easier to reason about, and more effective in practice, especially on smaller "tabular" datasets. What about generalized additive models, random forests, gradient boosted trees, etc.?
The author of the document I'm sure is aware of these techniques, and I assume they are left out because they didn't perform well enough to be considered here. But I don't think it does the discourse any favors to promulgate the false dichotomy.
However I don't like that there is often a strict dichotomy presented between "deep learning" and "statistics". There is a whole world of gray areas and hybrid techniques, which tend to be both more accessible, easier to reason about, and more effective in practice, especially on smaller "tabular" datasets. What about generalized additive models, random forests, gradient boosted trees, etc.?
The author of the document I'm sure is aware of these techniques, and I assume they are left out because they didn't perform well enough to be considered here. But I don't think it does the discourse any favors to promulgate the false dichotomy.