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IMPROVINGTHEEFFICIENCYOFTRAININGARTIFICIALINTELLIGENCEMODELSUSINGNUMERICALMETHODS

Thisstudyexploresmethodsforimprovingtheefficiencyoftrainingartificialintelligence(AI)modelsthroughnumericaltechniques.TheresearchfocusesonoptimizingneuralnetworksandsequentialmodelsusingGradientDescent,StochasticGradientDescent,andRunge–Kuttaalgorithms.Experimentalresultsdemonstratethatnume

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CreatorNargizaSadriddinovnaZokirova
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Published2026-04-08
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DOI10.5281/zenodo.19474910
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Downloads12
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Licensecc-by-4.0
File Size237.7 KB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views24
Total Downloads12

Thisstudyexploresmethodsforimprovingtheefficiencyoftrainingartificialintelligence(AI)modelsthroughnumericaltechniques.TheresearchfocusesonoptimizingneuralnetworksandsequentialmodelsusingGradientDescent,StochasticGradientDescent,andRunge–Kuttaalgorithms.Experimentalresultsdemonstratethatnumericalapproachessignificantlyenhancemodelaccuracy,reduceerrorrates,andacceleratethetrainingprocess.InthecontextofUzbekistan,integratingAImodelsintodigitaleducationplatformsenablespersonalizedandinteractivelearning,reducesteachers’workload,andimprovesstudentengagementandacademicperformance

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IMPROVINGTHEEFFICIENCYOFTRAININGARTIFICIALINTELLIGENCEMODELSUSINGNUMERICALMETHODS (Full Dataset)237.7 KB
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Files are hosted on the source repository. Click download to access the full dataset.

NargizaSadriddinovnaZokirova (2026). IMPROVINGTHEEFFICIENCYOFTRAININGARTIFICIALINTELLIGENCEMODELSUSINGNUMERICALMETHODS. https://doi.org/10.5281/zenodo.19474910
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