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In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network.
Digital learning is learning that is supported by technology. [1] It encompasses any type of learning that is accompanied by technology or by instructional practice that makes effective use of technology. It includes a wide array of practices, including blended and virtual learning. A variety of names began to be used to denote education ...
Learning rule. An artificial neural network 's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. It is done by updating the weights and bias levels of a network when a network is simulated in a ...
Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [2]
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.
E-learning literature identifies an ecology of concepts from a bibliometric study were identified the most used concepts associated with the use of computers in learning contexts, e.g., computer-assisted instruction (CAI), computer-assisted learning (CAL), computer-based education (CBE), e-learning, learning management systems (LMS), self ...
Reinforcement learning ( RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and ...
In computational learning theory, probably approximately correct ( PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.
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