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基於人工智慧卷積神經網路與機器學習混合模型之鑄件品質分類效能研究 = =...
~
鄭弘翊
基於人工智慧卷積神經網路與機器學習混合模型之鑄件品質分類效能研究 = = The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
基於人工智慧卷積神經網路與機器學習混合模型之鑄件品質分類效能研究 =/ 鄭弘翊.
Reminder of title:
The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models /
remainder title:
The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models.
Author:
鄭弘翊
Published:
雲林縣 :國立虎尾科技大學 , : 民114.12.,
Description:
[8], 46面 :圖, 表 ; : 30公分.;
Notes:
指導教授: 李孟樺.
Subject:
混合模型. -
Online resource:
電子資源
基於人工智慧卷積神經網路與機器學習混合模型之鑄件品質分類效能研究 = = The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models /
鄭弘翊
基於人工智慧卷積神經網路與機器學習混合模型之鑄件品質分類效能研究 =
The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models /The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models.鄭弘翊. - 初版. - 雲林縣 :國立虎尾科技大學 ,民114.12. - [8], 46面 :圖, 表 ;30公分.
指導教授: 李孟樺.
碩士論文--國立虎尾科技大學工業管理系工業工程與管理碩士班.
含參考書目.
(平裝).Subjects--Topical Terms:
1407327
混合模型.
基於人工智慧卷積神經網路與機器學習混合模型之鑄件品質分類效能研究 = = The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models /
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The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models /
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鄭弘翊.
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The Study of Casting Quality Classification Performance Based on AI Convolutional Neural Networks and Machine Learning Hybrid Models.
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初版.
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國立虎尾科技大學 ,
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民114.12.
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[8], 46面 :
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圖, 表 ;
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指導教授: 李孟樺.
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碩士論文--國立虎尾科技大學工業管理系工業工程與管理碩士班.
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混合模型.
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效能比較.
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電子資源
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圖書館B1F 博碩士論文專區
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1 records • Pages 1 •
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T014173
圖書館B1F 博碩士論文專區
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碩士論文(TM)
TM 008.169M 8710 114
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