{"title":"矩阵方程在深度学习中分辨率为m个数据有n个参数","authors":"Tshibengabu Tshimanga Yannick, Mbuyi Mukendi Eugène, Batubenga Mwamba-nzambi Jean-Didier","doi":"10.46565/jreas.202274400-403","DOIUrl":null,"url":null,"abstract":"This article on the vectorization of learning equations by neural network aims to give the matrix equations on [1-3]: first on the Z [8, 9] model of the perceptron[6] which calculates the inputs X, the Weights W and the bias, second on the quantization function [10] [11], called loss function [6, 7] [8]. and finally thegradient descent algorithm for maximizing likelihood and minimizing Z errors [4, 5].","PeriodicalId":14343,"journal":{"name":"International Journal of Research in Engineering and Applied Sciences","volume":"35 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MATRIX EQUATIONS IN DEEP LEARNING RESOLUTION FOR M DATA HAS N PARAMETERS\",\"authors\":\"Tshibengabu Tshimanga Yannick, Mbuyi Mukendi Eugène, Batubenga Mwamba-nzambi Jean-Didier\",\"doi\":\"10.46565/jreas.202274400-403\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article on the vectorization of learning equations by neural network aims to give the matrix equations on [1-3]: first on the Z [8, 9] model of the perceptron[6] which calculates the inputs X, the Weights W and the bias, second on the quantization function [10] [11], called loss function [6, 7] [8]. and finally thegradient descent algorithm for maximizing likelihood and minimizing Z errors [4, 5].\",\"PeriodicalId\":14343,\"journal\":{\"name\":\"International Journal of Research in Engineering and Applied Sciences\",\"volume\":\"35 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Research in Engineering and Applied Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46565/jreas.202274400-403\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Research in Engineering and Applied Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46565/jreas.202274400-403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MATRIX EQUATIONS IN DEEP LEARNING RESOLUTION FOR M DATA HAS N PARAMETERS
This article on the vectorization of learning equations by neural network aims to give the matrix equations on [1-3]: first on the Z [8, 9] model of the perceptron[6] which calculates the inputs X, the Weights W and the bias, second on the quantization function [10] [11], called loss function [6, 7] [8]. and finally thegradient descent algorithm for maximizing likelihood and minimizing Z errors [4, 5].
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