neural network - significado y definición. Qué es neural network
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Qué (quién) es neural network - definición

COMPUTATIONAL MODEL USED IN MACHINE LEARNING, BASED ON CONNECTED, HIERARCHICAL FUNCTIONS
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  • An artificial neural network is an interconnected group of nodes, inspired by a simplification of [[neuron]]s in a [[brain]]. Here, each circular node represents an [[artificial neuron]] and an arrow represents a connection from the output of one artificial neuron to the input of another.
  • Neuron and myelinated axon, with signal flow from inputs at dendrites to outputs at axon terminals
  • Confidence analysis of a neural network

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STRUCTURE IN BIOLOGY AND ARTIFICIAL INTELLIGENCE
Neural networks; History of neural networks; Applications of neural networks
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STRUCTURE IN BIOLOGY AND ARTIFICIAL INTELLIGENCE
Neural networks; History of neural networks; Applications of neural networks
(neural networks)
In computing, a neural network is a program or system which is modelled on the human brain and is designed to imitate the brain's method of functioning, particularly the process of learning.
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STRUCTURE IN BIOLOGY AND ARTIFICIAL INTELLIGENCE
Neural networks; History of neural networks; Applications of neural networks
(also neural net)
¦ noun a computer system modelled on the human brain and nervous system.

Wikipedia

Artificial neural network

Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.

Ejemplos de uso de neural network
1. The bulk of Wikipedia‘s errors came from a few entries: "Dmitry Mendeleev", "Acheulean industry" "neural network" and "prion" were the worst offenders.
2. "The point of a brain is that it‘s not one huge neural network with feedback, it has up to 50 to 60 identified areas, all of which have feedback and all of which are capable of knowledge storage.
3. An artificial neural network could be made to switch on when it "sees" something it knows – a tortoise, say – and it will learn to associate a certain pattern of neuron activity with it.
4. "We also found out digitally that Van Gogh used those combinations like blue next to yellowy–orange to highlight certain things in his paintings – a female figure wearing a blue dress, for example, and the background of bright orange." The next step is to attach a neural network to the software so that it can learn further details about a painter.