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Artificial neural networks are typically specified using three things: For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image.
Like other machine learning methods – systems that learn from data – neural networks have been used to solve a wide variety of tasks, like computer vision and speech recognition, that are hard to solve using ordinary rule-based programming.Artificial neural networks are similar to biological neural networks in the performing by its units of functions collectively and in parallel, rather than by a clear delineation of subtasks to which individual units are assigned.The term "neural network" usually refers to models employed in statistics, cognitive psychology and artificial intelligence.Neural network models which command the central nervous system and the rest of the brain are part of theoretical neuroscience and computational neuroscience. For networks of living neurons, see Biological neural network. For the evolutionary concept, see Neutral network (evolution).An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.
Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another.
In machine learning and cognitive science, artificial neural networks (ANNs) are a family of models inspired by biological neural networks (the central nervous systems of animals, in particular the brain) which are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.
Examinations of humans' central nervous systems inspired the concept of artificial neural networks.
In an artificial neural network, simple artificial nodes, known as "neurons", "neurodes", "processing elements" or "units", are connected together to form a network which mimics a biological neural network.
There is no single formal definition of what an artificial neural network is.
However, a class of statistical models may commonly be called "neural" if it possesses the following characteristics: The adaptive weights can be thought of as connection strengths between neurons, which are activated during training and prediction.