Faster access than browser!Similarities between Artificial neural network and Artificial neuron
Artificial neural network and Artificial neuron have 22 things in common (in Unionpedia): Action potential, Activation function, ADALINE, Artificial neuron, Backpropagation, Connectionism, Differentiable function, Feedforward neural network, Frank Rosenblatt, Function approximation, Gradient descent, Multilayer perceptron, Neuron, Paul Werbos, Perceptron, Principal component analysis, Rectifier (neural networks), Sigmoid function, Vector (mathematics and physics), Walter Pitts, Warren Sturgis McCulloch, Weighting.
Action potential
In physiology, an action potential occurs when the membrane potential of a specific axon location rapidly rises and falls: this depolarisation then causes adjacent locations to similarly depolarise.
Action potential and Artificial neural network · Action potential and Artificial neuron ·
Activation function
In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs.
Activation function and Artificial neural network · Activation function and Artificial neuron ·
ADALINE
ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network.
ADALINE and Artificial neural network · ADALINE and Artificial neuron ·
Artificial neuron
An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network.
Artificial neural network and Artificial neuron · Artificial neuron and Artificial neuron ·
Backpropagation
Backpropagation is a method used in artificial neural networks to calculate a gradient that is needed in the calculation of the weights to be used in the network.
Artificial neural network and Backpropagation · Artificial neuron and Backpropagation ·
Connectionism
Connectionism is an approach in the fields of cognitive science, that hopes to represent mental phenomena using artificial neural networks.
Artificial neural network and Connectionism · Artificial neuron and Connectionism ·
Differentiable function
In calculus (a branch of mathematics), a differentiable function of one real variable is a function whose derivative exists at each point in its domain.
Artificial neural network and Differentiable function · Artificial neuron and Differentiable function ·
Feedforward neural network
A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle.
Artificial neural network and Feedforward neural network · Artificial neuron and Feedforward neural network ·
Frank Rosenblatt
Frank Rosenblatt (July 11, 1928July 11, 1971) was an American psychologist notable in the field of artificial intelligence.
Artificial neural network and Frank Rosenblatt · Artificial neuron and Frank Rosenblatt ·
Function approximation
In general, a function approximation problem asks us to select a function among a well-defined class that closely matches ("approximates") a target function in a task-specific way.
Artificial neural network and Function approximation · Artificial neuron and Function approximation ·
Gradient descent
Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function.
Artificial neural network and Gradient descent · Artificial neuron and Gradient descent ·
Multilayer perceptron
A multilayer perceptron (MLP) is a class of feedforward artificial neural network.
Artificial neural network and Multilayer perceptron · Artificial neuron and Multilayer perceptron ·
Neuron
A neuron, also known as a neurone (British spelling) and nerve cell, is an electrically excitable cell that receives, processes, and transmits information through electrical and chemical signals.
Artificial neural network and Neuron · Artificial neuron and Neuron ·
Paul Werbos
Paul J. Werbos (born 1947) is a scientist best known for his 1974 Harvard University Ph.D. thesis, which first described the process of training artificial neural networks through backpropagation of errors.
Artificial neural network and Paul Werbos · Artificial neuron and Paul Werbos ·
Perceptron
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).
Artificial neural network and Perceptron · Artificial neuron and Perceptron ·
Principal component analysis
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
Artificial neural network and Principal component analysis · Artificial neuron and Principal component analysis ·
Rectifier (neural networks)
In the context of artificial neural networks, the rectifier is an activation function defined as the positive part of its argument: f(x).
Artificial neural network and Rectifier (neural networks) · Artificial neuron and Rectifier (neural networks) ·
Sigmoid function
A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve.
Artificial neural network and Sigmoid function · Artificial neuron and Sigmoid function ·
Vector (mathematics and physics)
When used without any further description, vector usually refers either to.
Artificial neural network and Vector (mathematics and physics) · Artificial neuron and Vector (mathematics and physics) ·
Walter Pitts
Walter Harry Pitts, Jr. (23 April 1923 – 14 May 1969) was a logician who worked in the field of computational neuroscience.
Artificial neural network and Walter Pitts · Artificial neuron and Walter Pitts ·
Warren Sturgis McCulloch
Warren Sturgis McCulloch (November 16, 1898 – September 24, 1969) was an American neurophysiologist and cybernetician, known for his work on the foundation for certain brain theories and his contribution to the cybernetics movement.
Artificial neural network and Warren Sturgis McCulloch · Artificial neuron and Warren Sturgis McCulloch ·
Weighting
The process of weighting involves emphasizing the contribution of some aspects of a phenomenon (or of a set of data) to a final effect or result, giving them more weight in the analysis.
Artificial neural network and Weighting · Artificial neuron and Weighting ·
The list above answers the following questions
- What Artificial neural network and Artificial neuron have in common
- What are the similarities between Artificial neural network and Artificial neuron
Artificial neural network and Artificial neuron Comparison
Artificial neural network has 329 relations, while Artificial neuron has 60. As they have in common 22, the Jaccard index is 5.66% = 22 / (329 + 60).
References
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