A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms

In order to improve the talent training effect of electrical courses, this paper proposes a talent training model for electrical courses considering diverse constraint models and knowledge recognition algorithms. In order to obtain better performance of traditional deep learning models, it is usuall...

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Main Author: Fanping Min
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Advances in Multimedia
Online Access:http://dx.doi.org/10.1155/2022/5947573
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author Fanping Min
author_facet Fanping Min
author_sort Fanping Min
collection DOAJ
description In order to improve the talent training effect of electrical courses, this paper proposes a talent training model for electrical courses considering diverse constraint models and knowledge recognition algorithms. In order to obtain better performance of traditional deep learning models, it is usually necessary to increase the parameter scale of traditional deep learning models. Pretrained language models can be trained unsupervised directly using unlabeled corpora to learn vector representations of words without using labeled datasets. In addition, this paper uses the knowledge base and alias dictionary to build a knowledge graph and constructs a teaching model for electrical courses considering diverse constraint models and knowledge recognition algorithms. Through the research, it can be seen that the experimental teaching model of electrical courses proposed in this paper considering diverse constraint models and knowledge recognition algorithms has a very good effect on talent training.
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spelling doaj-art-12fb8bf48708488fa950c35e51468f622025-08-20T02:01:42ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/5947573A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition AlgorithmsFanping Min0Institute of Higher EducationIn order to improve the talent training effect of electrical courses, this paper proposes a talent training model for electrical courses considering diverse constraint models and knowledge recognition algorithms. In order to obtain better performance of traditional deep learning models, it is usually necessary to increase the parameter scale of traditional deep learning models. Pretrained language models can be trained unsupervised directly using unlabeled corpora to learn vector representations of words without using labeled datasets. In addition, this paper uses the knowledge base and alias dictionary to build a knowledge graph and constructs a teaching model for electrical courses considering diverse constraint models and knowledge recognition algorithms. Through the research, it can be seen that the experimental teaching model of electrical courses proposed in this paper considering diverse constraint models and knowledge recognition algorithms has a very good effect on talent training.http://dx.doi.org/10.1155/2022/5947573
spellingShingle Fanping Min
A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms
Advances in Multimedia
title A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms
title_full A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms
title_fullStr A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms
title_full_unstemmed A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms
title_short A Talent Training Model for Electrical Courses considering Diverse Constraint Models and Knowledge Recognition Algorithms
title_sort talent training model for electrical courses considering diverse constraint models and knowledge recognition algorithms
url http://dx.doi.org/10.1155/2022/5947573
work_keys_str_mv AT fanpingmin atalenttrainingmodelforelectricalcoursesconsideringdiverseconstraintmodelsandknowledgerecognitionalgorithms
AT fanpingmin talenttrainingmodelforelectricalcoursesconsideringdiverseconstraintmodelsandknowledgerecognitionalgorithms