6239 - 深度學習理論與實務
Deep Learning: Theory and Application
教育目標 Course Target
本課程旨在培養學生具備深度學習（Deep Learning）之理論基礎、模型設計能力與實務應用能力，使學生能夠理解人工智慧核心演算法之原理，並具備實際建構、訓練與部署深度學習模型之能力。課程內容涵蓋人工神經網路架構、模型訓練方法、最佳化技術與現代深度學習應用，並由機器學習基礎開始，逐步深入深度神經網路之數學原理與實作技術，結合理論推導、實際案例與專題開發，探討神經網路架構、模型最佳化、電腦視覺、自然語言處理及生成式 AI 等重要主題，協助學生建立完整之 AI 模型開發流程與人工智慧系統開發能力。
This course aims to cultivate students' theoretical foundation, model design ability and practical application ability of deep learning, so that students can understand the principles of core algorithms of artificial intelligence and have the ability to actually construct, train and deploy deep learning models. The course content covers artificial neural network architecture, model training methods, optimization technology and modern deep learning applications. It starts with the basics of machine learning and gradually deepens into the mathematical principles and implementation techniques of deep neural networks. It combines theoretical derivation, practical cases and topic development to explore important topics such as neural network architecture, model optimization, computer vision, natural language processing and generative AI, helping students establish a complete AI model development process and artificial intelligence system development capabilities.
參考書目 Reference Books
自備講義
Prepare your own handouts
評分方式 Grading
| 評分項目 Grading Method |
配分比例 Percentage |
說明 Description |
|---|---|---|
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期中報告 interim report |
30 | |
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期末專題 Final topic |
40 | |
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作業與討論 Assignments and discussions |
20 | |
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出席 Attend |
10 |
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: 6239
- 學分 Credit: 3-0
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上課時間 Course Time:Wednesday/2,3,4[M007]
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授課教師 Teacher:賴翌維
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修課班級 Class:資管系3,4,碩1,2
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選課備註 Memo:資管34可修，電腦教室
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