REVIEW OF ACTIVATION FUNCTIONS IN NEURAL NETWORKS

Volume 17 No 7 (2019)

Oshank Kashyap, Kriti Kumari, Kartikay Yadav, Jatin Baweja, Vintee Vats, Mohit Sharma

DOI: 10.14704/nq.2022.20.7.NQ33217

Abstract

A neural network is a machine-learning solid technique that mimics human brain activity. It consists of various layers, including an input, an output, and sometimes multiple hidden layers. In a human brain, all of the information in the cell is summed up, with the frequency of incoming impulses fluctuating. The activation function determines the transmission of a signal to the next layer or the activation of a neuron. A threshold function processes the input sum in the neural network and provides an output signal. The role and types of activation functions in neural networks will be discussed in this paper

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