Usually you need multiple layers when you train NN directly from raw features (like pixels or words) for perception-like problems. In that case you want to lear

Usually you need multiple layers when you train NN directly from raw features (like pixels or words) for perception-like problems. In that case you want to learn complicated multi-stage transformations to get useful high-level features. If you work with already extracted features (like a classical ML pipeline where you can use SVMs or logistic regression as well), you may go with one-layer network. But deep learning really shines with perception-like problem. Don't be fooled by the approximation theorem (you can approximate any function with only one hidden layer). It is beautiful in theory, but in practice it is like saying that you can write any program in brainfuck because it is turing-complete.