This chapter introduces deep learning (DL) in the framework of experimentalism, taking inspiration from Pierre Oleron’s explanation of human intellectual activities in terms of long (or, deep) circuits. A history of DL is presented, from its origin in the mid-twentieth century to the breakthrough of deep neural networks (DNNs) in the last decades. Architectural and representational issues are then discussed in depth. Convolutional neural networks, the most popular and successful DL algorithm to date, are reviewed in detail. Finally, adaptive activation functions in DNNs are presented in the context of homeostatic neuroplasticity, surveyed, and analyzed.
Gori, M., Precioso, F., Trentin, E. (2023). Deep Learning. In R. Sun (a cura di), The Cambridge Handbook of Computational Cognitive Sciences (pp. 301-349). Cambridge University Press [10.1017/9781108755610.012].
Deep Learning
M. Gori;E. Trentin
2023-01-01
Abstract
This chapter introduces deep learning (DL) in the framework of experimentalism, taking inspiration from Pierre Oleron’s explanation of human intellectual activities in terms of long (or, deep) circuits. A history of DL is presented, from its origin in the mid-twentieth century to the breakthrough of deep neural networks (DNNs) in the last decades. Architectural and representational issues are then discussed in depth. Convolutional neural networks, the most popular and successful DL algorithm to date, are reviewed in detail. Finally, adaptive activation functions in DNNs are presented in the context of homeostatic neuroplasticity, surveyed, and analyzed.
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https://hdl.handle.net/11365/1254478