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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.
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] A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain ...
A physical neural network is a specific type of neuromorphic hardware that relies on electrically adjustable materials, such as memristors, to emulate the function of neural synapses. The term "physical neural network" highlights the use of physical hardware for computation, as opposed to software-based implementations. It broadly refers to ...
This glossary provides precise definitions for a wide range of AI topics that you may find in business, from foundational principles like machine learning and neural networks to advanced topics ...
It is commonly used to train deep neural networks, [39] a term referring to neural networks with more than one hidden layer. [40] backpropagation through structure (BPTS) A gradient-based technique for training recurrent neural networks, proposed in a 1996 paper written by Christoph Goller and Andreas Küchler. [41] backpropagation through time ...
Networks such as the previous one are commonly called feedforward, because their graph is a directed acyclic graph. Networks with cycles are commonly called recurrent. Such networks are commonly depicted in the manner shown at the top of the figure, where is shown as dependent upon itself. However, an implied temporal dependence is not shown.
A neural network, also called a neuronal network, is an interconnected population of neurons (typically containing multiple neural circuits). [1] Biological neural networks are studied to understand the organization and functioning of nervous systems .
Some artificial neural networks are adaptive systems and are used for example to model populations and environments, which constantly change. Neural networks can be hardware- (neurons are represented by physical components) or software-based (computer models), and can use a variety of topologies and learning algorithms.