What is Neural Networks

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AI method that mimics the function of the brain, by forming a layered network of artificial neurons organized in different layers (input, hidden, and output layers). Learn more in: Challenges and Opportunities of Soft Computing Tools in Health Care Delivery

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AI method that mimics the function of the brain, by forming a layered network of artificial neurons organized in different layers (input, hidden, and output layers). Learn more in: Challenges and Opportunities of Soft Computing Tools in Health Care Delivery

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Biological-inspired computational models that mimic the brain structure with simple processing units organized in layers that are highly connected. Learn more in: Time Series Forecasting in Retail Sales Using LSTM and Prophet

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Biologically inspired computing system based on the neural structure of the brain. Learn more in: An Exploration of Backpropagation Numerical Algorithms in Modeling US Exchange Rates

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Form of artificial intelligence. Learn more in: Patients on Weaning Trials Classified with Neural Networks and Features Selection

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Programs that simulate a network of communicating nerve cells to achieve a machine learning objective Learn more in: Artificial Intelligence and Investing

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Network system, modeled after the human brain, capable of learning from data. Learn more in: The Summers and Winters of Artificial Intelligence

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Programs that simulate a network of communicating nerve cells to achieve a machine learning objective. Learn more in: Applying Artificial Intelligence to Financial Investing

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Are typically composed of multiple layers and the signal traverses from the input layer to the output layer of neurons. Trained neural networks can approximate an arbitrary nonlinear function. Learn more in: An Efficient Learning of Neural Networks to Acquire Inverse Kinematics Model

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A network of computational nodes which are interconnected by links. Instead of capturing rules like an expert system does, neural network relies on using input/output data patterns to train the network. Its links are modified to capture the knowledge, so that after it has been adequately trained, it can provide appropriate responses to new input data. Learn more in: PMA Supplier Selection Using the Mahalanobis Taguchi System

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Are thought to form the basal structure enabling brain function. Artificial neural networks adopt those wiring structures to solve problems in machine learning and pattern recognition on computers. Learn more in: Analysis of Large-Scale OMIC Data Using Self Organizing Maps

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It includes machine learning with deep learning methods that utilizes huge amounts of training data to detect association among various variables. Learn more in: Artificial Intelligence and Machine Learning Algorithms

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A graphical representation of a nonlinear function. Usually represented as a directed acyclic graph. 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. Learn more in: Conditional Hazard Estimating Neural Networks

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Computing systems that are composed of many simple processing elements operating in parallel whose function is determined by network structure. They are used mainly to model functional relationship among many variables. Learn more in: Neural Networks for Retail Sales Forecasting

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Programs that simulate a network of communicating nerve cells to achieve a machine learning objective. Learn more in: Artificial Intelligence and Investing

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Also referred to as artificial intelligence (AI), which utilizes predictive algorithms. Learn more in: Applications of Data Mining in the Healthcare Industry

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A powerful set of algorithms whose objective is to find a pattern of behavior. They are called neural because they are based on how biological neurons work when processing information. These networks try to simulate the way the neural network of a live being processes, recognizes and transmits the information. The implementation of neural networks in very different fields is due to their good performance relative to other methods Learn more in: Using Self Organizing Maps for Banking Oversight: The Case of Spanish Savings Banks

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Generally known as Artificial Neural Networks. Computational models that are built as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Learn more in: Prediction of Major Earthquakes as Rare Events Using RF-Typed Polynomial Neural Networks

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Also referred to as artificial intelligence (AI), which utilizes predictive algorithms. Learn more in: Data Mining Fundamental Concepts and Critical Issues

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Computational models, inspired in biological networks, that learn and make a prediction based on algorithms. Learn more in: Preference Reversal Under Vulnerability: An Application of Neural Networks in Mexican Family Firms

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Also referred to as artificial intelligence (AI), which utilizes predictive algorithms. Learn more in: Data Mining: Payoffs and Pitfalls

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A black-box nonlinear model whose main characteristics are to be composed by many nonlinear elements of the same simple kind composed in a regular structure whose parameters are identified from examples of data. Learn more in: Attention Facilitation via Multimedia Stimulation

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Also called “neural nets,” they are simplified models of the brain composed of large numbers of units (the analogs of neurons) together with weights that measure the strength of connections between the units. Learn more in: Walking the Information Overload Tightrope

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It is an art of imitating human brain activities through artificial intelligence. Learn more in: Watermarking Using Artificial Intelligence Techniques

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Programs that simulate a network of communicating nerve cells to achieve a machine learning objective. Learn more in: Applying Artificial Intelligence to Financial Investing

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Typically composed of multiple layers and the signal traverses from the input layer to the output layer of neurons. Trained neural networks can approximate an arbitrary nonlinear function. Learn more in: Neural Networks to Solve Nonlinear Inverse Kinematic Problems

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A set of artificial neurons with tunable gains, which are linked to estimate unknown functions or parameters. Learn more in: Adaptive Swarm Coordination and Formation Control

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A highly interconnected set of neurons that form a brain in animals and is often emulated in computer programs because of their power. Learn more in: Advanced Methodologies Descriptions and Applications

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A class of machine learning algorithm that works on the structure of biological nervous systems. A class of machine learning algorithm consisting of multiple nodes that communicate through their connecting synapses. Learn more in: A Bayesian Network Model for Probability Estimation

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Learning models based on the structure and processing of the nervous system. Learn more in: Machine Learning

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A neural network is “a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use.” (Haykin, 1994). Learn more in: From Analysis to Estimation of User Behavior