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- W4387606739 abstract "To understand and build a neural network, one may compare the structure and function of neurons in the brain to the artificial neurons in a mathematically constructed neural topology. Perception is the simplest type of artificial neural network, made up of a single layer of input nodes connected to output nodes, used for binary classification tasks. Activation functions are mathematical functions that are applied to the output of a neuron in a neural network to introduce nonlinearity in the network's decision-making process. This chapter discusses the analogy of biological and artificial neurons and their similarities in structure and function. It describes how the structure of neurons in the brain can be mimicked in artificial neural networks and how this has led to the development of powerful machine-learning algorithms and also explores the structure of neural networks, including the derivative equations used in backpropagation, which train the network to learn from data." @default.
- W4387606739 created "2023-10-14" @default.
- W4387606739 date "2023-10-13" @default.
- W4387606739 modified "2023-10-14" @default.
- W4387606739 title "The Basics of Perceptron" @default.
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- W4387606739 doi "https://doi.org/10.1002/9781394217519.ch3" @default.
- W4387606739 hasPublicationYear "2023" @default.
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