L. Docheva, I. Dochev, StoychoVelizarov Manev, Ludvig Lubih
{"title":"The Process of Teaching Students in the Deep Neural Network Laboratory Work","authors":"L. Docheva, I. Dochev, StoychoVelizarov Manev, Ludvig Lubih","doi":"10.1109/TELECOM56127.2022.10017260","DOIUrl":null,"url":null,"abstract":"The various software availability for deep neural network implementation enables students easy and accessible implementation of different tasks in a wide area range. The ready for use applications using, for many of the deep neural network training stages is not related to a deeper understanding of the deep neural network theory. For this reason, the authors of this paper have developed an exercise with students' participation as many of the stages of the deep neural network training as possible. This paper presents one solution for laboratory exercise process organization in distance learning in deep neural network area. Each stage of the student's work is examined in detail. The specifics of its distance learning implementation are also discussed.","PeriodicalId":359231,"journal":{"name":"2022 30th National Conference with International Participation (TELECOM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 30th National Conference with International Participation (TELECOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TELECOM56127.2022.10017260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The various software availability for deep neural network implementation enables students easy and accessible implementation of different tasks in a wide area range. The ready for use applications using, for many of the deep neural network training stages is not related to a deeper understanding of the deep neural network theory. For this reason, the authors of this paper have developed an exercise with students' participation as many of the stages of the deep neural network training as possible. This paper presents one solution for laboratory exercise process organization in distance learning in deep neural network area. Each stage of the student's work is examined in detail. The specifics of its distance learning implementation are also discussed.