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Part of the book series: Synthesis Lectures on Computer Vision ((SLCV))

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Abstract

Before going into the details of the CNNs, we provide in this chapter an introduction to artificial neural networks, their computational mechanism, and their historical background. Neural networks are inspired by the working of cerebral cortex in mammals. It is important to note, however, that these models do not closely resemble the working, scale and complexity of the human brain. Artificial neural network models can be understood as a set of basic processing units, which are tightly interconnected and operate on the given inputs to process the information and generate desired outputs. Neural networks can be grouped into two generic categories based on the way the information is propagated in the network.

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Khan, S., Rahmani, H., Shah, S.A.A., Bennamoun, M. (2018). Neural Networks Basics. In: A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-031-01821-3_3

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