  Convoulutional Neural Networks (CNNs) exhibit extraordinary performance on a
variety of machine learning tasks. However, their mathematical properties and
behavior are quite poorly understood. There is some work, in the form of a
framework, for analyzing the operations that they perform. The goal of this
project is to present key results from this theory, and provide intuition for
why CNNs work.

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