The primary task of many applications is approximating/estimating a function through samples drawn from a probability distribution on the input space.
The deep approximation is to approximate a function by compositions of many layers of simple functions, that can be viewed as a series of nested feature extractors. The key idea of deep learning network is to convert layers of compositions to layers of tunable parameters that can be adjusted through a learning process, so that it achieves a good approximation with respect to the input data. In this talk, we shall discuss mathematical theory behind this new approach of approximation; how it differs from the classic approximation theory, and how this new theory can be used to understand and design deep learning network.
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