What is Artificial Neural Network

Structure composes of a group of interconnected artificial neurons or units. The objective of a NN is to transform the inputs into meaningful outputs.

A mathematical model or a computational model that is inspired by the structure and/or functional aspects of biological neural networks.

A representation of knowledge as a network of interconnected artificial neurons. The network expresses complex relationships between inputs and outputs, it can also be used to group together similar input examples.

An artificial neural network (ANN) is a data handling worldview that is roused by the way organic sensory systems, for example, the cerebrum, process data. The key component of this worldview is the novel structure of the data handling framework. It is made out of an extensive number of exceptionally interconnected preparing components (neurones) working as one to take care of particular issues. ANNs, similar to individuals, learn by case. An ANN is designed for a particular application, for example, design acknowledgment or information grouping, through a learning procedure. Learning in natural frameworks includes changes in accordance with the synaptic associations that exist between the neurons.

A network of many simple processors (“units” or “neurons”) that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data; and are used in applications such as robotics, speech recognition, and signal processing or medical diagnosis.

A computing system inspired by the biological neural networks that constitute a human brain.

Computing system designed to simulate the way the human brain analyses and processes information.

A network composed of artificial neurons. Artificial neural networks can be trained to find nonlinear relationships in data.

Artificial Neural Network is actually modeled is a computational model which mimic the human brains works. There are units in the ANN called neurons these units are connected to other by link and every link is associated with a weight.

An artificial neural network is an information processing system which is inspired by the human nervous system for information processing. It can be trained to simulate a number of outputs in response to provided inputs.

ANN is a computational model consisting of an interconnected group of artificial neurons similar to biological neural networks.

Mathematical models that represent the interconnection between elements defined artificial neurons, i.e. mathematical constructs that to some extent mimic the properties of living neurons. These mathematical models can be used both to obtain an understanding of biological neural networks, but even more to solve engineering problems of artificial intelligence such as those that arise in various technological fields (in electronics, computer science, simulation, and other disciplines).

a computational model and copy the way human brains works. There are units in the ANN called neurons these units are connected to other by link and every link is associated with a weight.

A framework for handling machine learning procedures critically. More specifically, it is one of the best ways to realize a certain pattern automatically.

The network that uses some functions and tries to mimic the function of the human brain is called an artificial neural network.

An algorithm which can be used to estimate or classify inputs. Neural networks are capable of machine learning and pattern recognition. They are broadly used in data mining and predictive analytics.

Artificial Neural Networks are the modeling technique which was inspired from the central nervous system, capable of machine learning and pattern recognition.

An organized set of many simple processors called neurons that imitates a biological neural configuration.

An interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation.

An artificial neural network is an information processing system which is inspired by the human nervous system for information processing. It can be trained to simulate a number of outputs in response to provided inputs.

An ICT tool that imitates the real environment, providing various estimations and emphasizing how the system could react under certain circumstances.

A kind of machine learning algorithms loosely based on how biological neural networks work.

ANN is a branch of machine learning by employing the principles of neuronal organization discovered by connectionism in biological neural networks.

Structures comprised of densely interconnected simple processing elements, the artificial neurons, or nodes, which are capable of performing massively parallel computations and knowledge representation.

An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks.

An artificial neural network (ANN) is information dealing with the perspective that is awakened by the way natural tactile frameworks, for instance, the cerebrum, process information. The key part of this perspective is the novel structure of the information taking care of the system. It is made out of a broad number of extraordinarily interconnected planning segments (neurons) filling in as one to deal with specific issues. ANNs, like people, learn by case. An ANN is intended for a specific application, for instance, outline affirmation or data gathering, through a learning technique. Learning in characteristic systems incorporates changes as per the synaptic affiliations that exist between the neurons.

Artificial neural networks (ANNs) are computer systems that imitate an animal’s biological neural networks.

A machine learning technique based on the working of the human brain.

(ANN): A computational model inspired by the structure of biological neural networks. In ANNs, artificial neurons serve as simplified representations of their biological counterparts, offering unique processing capabilities compared to traditional mathematical models.

An artificial neural network is a computational model that mimics the way nerve cells work in the human brain.

A network of many simple processors, called “units” or “neurons”, which provides a simplified model of a biological neural network. The neurons are connected by links that carry numeric values corresponding to weightings and are usually organised in layers. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis.

A networked structure, modelled after a biological neural network, and implemented in software on a computer. Artificial neural networks enable computers to handle imperfect (noisy) data sets, which is essential for robust performance in advanced recognition and classification tasks (handwriting recognition, weather prediction, control of complex movements in robotic bodies).

Interconnected group of artificial neurons that uses a mathematical or computational model for information processing. They are based on the function of biologic neurons. It involves a group of simple processing elements (the neurons) which can exhibit complex global behaviour, as result of the connections between the neurons.

Artificial Neural Networks are computational models composed of interconnected processing elements called neurons that receive inputs, process them using predefined activation functions, and produce outputs.

Information elaboration system, software, or hardware that is based on the biological nervous systems, and it is composed of code units called “nodes” or “artificial neurons.”

A computational method, which is inspired by biological nervous system and is used to estimate a selected variable from inputs in machine learning literature.

Artificial neural networks, usually simply called neural networks, are computing systems vaguely 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.

An artificial neural network (ANN) is information taking care of perspective that is animated by the way tangible natural frameworks, for instance, the cerebrum, process information. The key segment of this perspective is the novel structure of the information taking care of the system. It is made out of a broad number of incredibly interconnected getting ready segments (neurons) filling in as one to deal with specific issues. ANNs, like people, learn by case. An ANN is intended for a specific application, for instance, plan affirmation or data gathering, through a learning strategy. Learning in regular structures incorporates changes as per the synaptic affiliations that exist between the neurons.

It is an information processing model inspired by the form of the brain in which biological nervous systems, such as the brain, process information.

Artificial Neural Network is a structure framed with densely interconnected with artificial neurons that performs the parallel computations for data processing and knowledge representations.

The computational model based on the structure and function of biological neural networks.

(commonly referred to as “neural network” or “neural net” ) A computer architecture, implemented in either hardware or software, modeled after biological neural networks. Nodes are connected in a manner suggestive of connections between the biological neurons they represent. The resulting network “learns” through directed trial and error. Most neural networks have some sort of “training” algorithm to adjust the weights of connections between nodes on the basis of patterns found in sample or historical data.

A biologically inspired sub-field of artificial intelligence based on the brain is called an “artificial neural network.” An ANN is a network of linked nodes inspired by the simplicity of neurons in the brain. ANNs include input, hidden, and output layers with linked neurons (nodes) to replicate the human brain.

Structure for distributed and parallel processing of information, formed by a series of units (which may possess a local memory and make local information processing operations), interconnected via one-way communication channels, called connections.

Artificial neural network is a collection of connected input/output units called neurons. Each connection has a weight associated with it to develop and test computational analysis of neurons. This neural network learns by adjusting these weights iteratively till it is able to predict the correct class label of the input data.

Adaptive mathematical model based on the interconnection of elements, called artificial neurons, that aims to obtain an estimation of the output variables of the system by learning from given examples of the input variables. They can be used for function approximation, classification, pattern recognition, etc.

Artificial Neural Networks (ANN) are computational models, originally inspired by biological neural networks. An ANN is a nonlinear statistical data modeling tool composed by a set of units (usually arranged in layers) connected to each other via weighted edges. They are used to solve specific tasks by incrementally learning an unknown function of the input data and are usually trained with a number of known input-output pairs.

An artificial neural network is a system based on the functioning of biological neural networks, an emulation of the biological neural system.

Computing system inspired by neurons which can learn to convert a series of input features into a meaningful output.

A subset of machine learning whose structure is inspired by the human brain and they mimic the way biological neurons signal each other.

An artificial neural network (ANN) is a data handling worldview that is roused by the way organic sensory systems, for example, the cerebrum, process data. The key component of this worldview is the novel structure of the data handling framework. It is made out of an extensive number of exceptionally interconnected preparing components (neurones) working as one to take care of particular issues. ANNs, similar to individuals, learn by case. An ANN is designed for a particular application, for example, design acknowledgment or information grouping, through a learning procedure. Learning in natural frameworks includes changes in accordance with the synaptic associations that exist between the neurons.

A computational model that is inspired by the way biological neural networks in the human brain process information.

A set of connected artificial neurons.

ANNs are non-parametric computational tools which resembles the operation of biological nervous systems and work by learning from the surrounding.

Neural networks are a computational approach and based on massively parallel, distributed and adaptive systems, modeled on the general features of biological networks with the potential for ever improving performance through a dynamical learning process.

Information processing structure without global or shared memory that takes the form of a directed graph where each of the computing elements (“neurons”) is a simple processor with internal and adjustable parameters, that operates only when all its incoming information is available.

An interconnected group of artificial neurons, which are elements that use a mathematical model that reproduce, through a great simplification, the behaviour of a real neuron, used for distributed information processing. They are inspired by nature in order to achieve some characteristics presented in the real neural networks, such as error and noise tolerance, generalization capabilities, etc

A network of many simple processors (“units” or “neurons”) that imitates a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained to find nonlinear relationships in data, and are used in applications such as robotics, speech recognition, signal processing or medical diagnosis.

Computational models used in artificial intelligence.

ANNs are mathematical models that represent the interconnection between elements defined artificial neurons, i.e. mathematical constructs that to some extent mimic the properties of living neurons. These mathematical models can be used both to obtain an understanding of biological neural networks, but even more to solve engineering problems of artificial intelligence such as those that arise in various technological fields (in electronics, computer science, simulation, and other disciplines).

Information processing structure without global or shared memory that takes the form of a directed graph where each of the computing elements (“neurons”) is a simple processor with internal and adjustable parameters, that operates only when all its incoming information is available.

A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.

A highly nonlinear computing structure, modeled after the neuronal organization of the human brain, used for solving some of the most complex problems in science and engineering, including regression, classification, and pattern recognition. A typical artificial neural network consists of an input layer, an output layer, as well as one or more hidden layers with connections between the neurons at each layer.

a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use.

Information processing structure without global or shared memory that takes the form of a directed graph where each of the computing elements (“neurons”) is a simple processor with internal and adjustable parameters, that operates only when all its incoming information is available.

A computing system inspired by the biological neural networks that constitute a human brain.

A tool of nonlinear statistical data modeling where relationships between inputs and outputs can be modeled or patterns can be found.