how many layers is it considered Deep Learning?

University of Michigan - Ann Arbor

A neural network configuration is called "shallow" if it has only a few hidden layers. In contrast, it's called "deep" if it has many hidden layers.

Deep vs. Shallow Neural Networks

A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images: 
https://www.frontiersin.org/articles/10.3389/fninf.2019.00053/full

A Comparison of Shallow and Deep Learning Methods for Predicting Cognitive Performance of Stroke Patients From MRI Lesion Images

Because deeper networks can learn more complicated functions. Exponentially more nodes would be needed in each layer to achieve the same level of complexity if the network was shallower.

Why deep networks instead of shallower networks?

In this diagram, what do the horizontal axis (x-axis) and vertical axis (y-axis) represent? 

Which are the following are true about the size of neural networks? (Use this plot as a reference.)

Deep Learning is a sub-class of Machine Learning algorithms whose peculiarity is a higher level of complexity. So, Deep Learning belongs to Machine Learning and they are absolutely not opposite concepts. We refer to shallow learning to those techniques of machine learning that are not deep.
This increment in the number of layers and the networks’ complexity is known as deep learning, something like a conventional network on steroids.

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