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A graph neural network (GNN) is an artificial neural network for processing data that can be represented as graphs. Learn about the architecture, message passing, and applications of GNNs, as well as their limitations and open questions.
Learn about the history, devices and services of turn-by-turn navigation, a feature of some satellite navigation devices that provides spoken or visual directions. Find out which apps and websites offer turn-by-turn navigation, including Google Maps, HERE WeGo, Waze and more.
Predictive analytics is a form of business analytics that uses machine learning to generate a predictive model for certain business applications. It involves various statistical techniques such as data modeling, machine learning, AI, deep learning, and data mining to analyze current and historical data and make predictions about future or unknown events.
A graphical model is a probabilistic model that uses a graph to express the conditional dependence structure between random variables. Learn about different types of graphical models, such as Bayesian networks, Markov random fields, and factor graphs, and their applications in various fields of machine learning and data mining.
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies via a directed acyclic graph. Learn how to use Bayesian networks for inference, learning, causal reasoning, and dynamic modeling.
A classic example is in printed circuit manufacturing: scheduling of a route of the drill machine to drill holes in a PCB. In robotic machining or drilling applications, the "cities" are parts to machine or holes (of different sizes) to drill, and the "cost of travel" includes time for retooling the robot (single-machine job sequencing problem ...
Machine learning (ML) is a field of artificial intelligence that develops and studies algorithms that can learn from data and generalize to unseen data. ML has many applications in various fields, such as natural language processing, computer vision, and medicine, and is related to data mining, statistics, and neural networks.
Learn about the history, methods, and applications of social network analysis, a technique for investigating social structures through networks and graph theory. Find out how social network analysis is used in various disciplines such as sociology, computer science, and public health.
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