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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 weight and bias[broken anchor] levels of a network when it is simulated in a specific data…

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Explore the main themes, entities and connections around Learning rule. Start with the topic map, then use the sections below for research and deeper semantic analysis.

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Advanced semantic analysis

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Map overview Semantic statistics

Learning rule

Nodes32
Edges31
Triples4
Avg. degree1.94
Density0.0625
Components1

How this topic connects Entity context

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Learning rule

Top relations

related to Backpropagation · 4
Learning rule → Backpropagation Algorithm, Delta Learning Rule, It, Seppo Linnainmaa

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

learning rule network perceptron delta algorithm weights neural competitive output developed machine hebbian also similar used training applied data values

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Learning rulerelated to BackpropagationSeppo Linnainmaa0.60section
Learning rulerelated to BackpropagationBackpropagation Algorithm0.60section
Learning rulerelated to BackpropagationIt0.60section
Learning rulerelated to BackpropagationDelta Learning Rule0.60section

Related concept clusters Concept neighborhoods

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    Min side: 3
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