In the book Deep Learning with Python, François Chollet writes (section 1.2.6, page 18)
In practice, there are fast-diminishing returns to successive applications of shallow-learning methods, because the optimal first representation layer in a three-layer model isn't the optimal first layer in a one-layer or two-layer model. What is transformative about deep learning is that it allows a model to learn all layers of representation jointly, at the same time, rather than in succession (greedily, as it's called).
By shallow learning, we mean traditional machine learning models that aren't deep learning, such as support vector machines.
I understood the above as below.
Using a model with three-layer shallow-learning methods has the same output (predicted) value as using one-layer shallow learning method. The effect of using multiple layers of shallow learning methods is to 'increase running time or repetition'.
Did I understand properly?