I have read an interesting paper on limitations of machine learning models:
Scaling Learning Algorithms towards AI.
It mentions limitation of two-layer neural networks and
other two-layer models (SVMs).
These shallow models are unable to learn some functions
without an exponential number of components.
For example, to learn the parity function over N input bits,
they would need 2^{N} hidden neurons.

On the other hand, a deep model with N layers could compute the parity with just N components.

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