Deep Learning
Deep Learning
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.
- University of Siena Italy
deep learning, deep neural network, internal representation, convolutional neural network, adaptive activation function, deep neural network, deep learning, internal representation, convolutional neural network, adaptive activation function
deep learning, deep neural network, internal representation, convolutional neural network, adaptive activation function, deep neural network, deep learning, internal representation, convolutional neural network, adaptive activation function
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