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An artificial neural network (ANN) combines biological principles with advanced statistics to solve problems in domains such as pattern recognition and game-play. ANNs adopt the basic model of neuron analogues connected to each other in a variety of ways.
In the mathematical theory of artificial neural networks, universal approximation theorems are theorems [1] [2] of the following form: Given a family of neural networks, for each function from a certain function space, there exists a sequence of neural networks ,, … from the family, such that according to some criterion.
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.
A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models . While individual neurons are simple, many of them together in a network can perform complex tasks.
An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary unit of an artificial neural network. [1] The design of the artificial neuron was inspired by biological neural circuitry.
Aside from their empirical performance, activation functions also have different mathematical properties: Nonlinear When the activation function is non-linear, then a two-layer neural network can be proven to be a universal function approximator. [6] This is known as the Universal Approximation Theorem. The identity activation function does not ...
A neural network learns in a bottom-up way: it takes in a large number of examples while being trained and from the patterns in those examples infers a rule that seems to best account for the ...
ReLU is one of the most popular activation function for artificial neural networks, [3] and finds application in computer vision [4] and speech recognition [5] [6] using deep neural nets and computational neuroscience. [7] [8] [9] It was first used by Alston Householder in 1941 as a mathematical abstraction of biological neural networks. [10]