5447 - 深度學習導論
An Introduction to Deep Learning
教育目標 Course Target
本課程旨在介紹深度學習之基本理論、核心模型與實作方法，涵蓋神經網路架構、訓練機制與應用領域，使學生理解現代人工智慧技術之數學基礎與演算法原理。課程兼顧理論推導與程式實作，培養學生建立模型、分析結果與優化系統之能力，並為後續進階機器學習與人工智慧研究奠定基礎。
This course aims to introduce the basic theory, core models and implementation methods of deep learning, covering neural network architecture, training mechanisms and application fields, so that students can understand the mathematical foundation and algorithm principles of modern artificial intelligence technology. The course takes into account both theoretical derivation and program implementation, cultivating students' ability to build models, analyze results and optimize systems, and lay the foundation for subsequent advanced machine learning and artificial intelligence research.
參考書目 Reference Books
Burkov, Andriy. The hundred-page machine learning book. Vol. 1. Quebec City, QC, Canada: Andriy Burkov, 2019.
Burkov, Andriy. The hundred-page machine learning book. Vol. 1. Quebec City, QC, Canada: Andriy Burkov, 2019.
評分方式 Grading
| 評分項目 Grading Method |
配分比例 Percentage |
說明 Description |
|---|---|---|
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平時成績 usual results |
20 | |
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期末報告 Final report |
60 | |
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課堂筆記 Class Notes |
20 |
授課大綱 Course Plan
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相似課程 Related Courses
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課程資訊 Course Information
基本資料 Basic Information
- 課程代碼 Course Code: 5447
- 學分 Credit: 0-3
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上課時間 Course Time:Friday/2,3,4[ST527]
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授課教師 Teacher:林佳威
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修課班級 Class:應數系3,4,碩1,2
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選課備註 Memo:開放大學部應數系3.4年級選課
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