Deep Architectures
Abstract
This chapter covers foundations on feedforward neural networks and incorporates some developments on deep learning, which has become a central topic in machine learning. From the foundational side, the chapter deals with topics in computational geometry, circuit theory, circuit complexity, approximation theory, optimization theory, and statistics. An accurate analysis is given to appreciate the fundamental representational improvements connected with deep architectures. In particular, the different role of the nonlinear activation functions (including the rectifier) is discussed for the purposes of representation and learning. A section is devoted to convolutional networks that are proposed in a novel ...
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