| 課程編碼 Course Code | 中文課程名稱 Course Name (Chinese) | 英文課程名稱 Course Name (English) | 總學分數 Credits | 總時數 Hours |
|---|---|---|---|---|
| 3105205 | 深度學習數位影像分析 | Deep Learning for Digital Image Analysis | 3.0 | 3 |
| 中文概述 Chinese Description | 深度學習數位影像分析，在近代的製造、醫藥、農業、太空探索、運輸和通信系統等應用領域，正迅速發展中。本課程主要介紹深度學習數位影像分析基本理論基礎，以此概念實際應用到各領域中。課程將探索最先進的深度學習模型，如物件偵測、影像分類，和影像分割，提供實用的實務練習；最後討論數位影像分析深度學習的未來挑戰。課程也將介紹最新的公開影像數據集(例如MNIST，CIFAR-10和ImageNet)，並涵蓋用於深度學習中的影像數據前處理和樣本標籤註記，詳細探討深度學習模型的評估指標。本課程將使用Matlab和Python範例，實作教學練習，課程單元包含: ●數位影像分析簡介 ●深度學習基礎 ●影像分析領域之深度學習基本介紹 ●卷積神經網絡 ●訓練卷積神經網絡 ●遷移式學習 ●深度學習數位影像數據標籤註記 ●深度學習模型評估方式 ●深度學習在數位影像分析領域之挑戰 ●超引數優化 ●激活函式 ●損失函式 ●優化函式 | |||
| 英文概述 English Description | This course covers the basics of deep learning for digital image analysis, including theoretical concepts and practical applications. Students will learn about object detection, image classification, and segmentation using state-of-the-art deep learning models. The course will also address the challenges of using deep learning in digital image analysis and cover popular image datasets such as MNIST, CIFAR-10, and ImageNet. Additionally, students will gain hands-on experience in image data preparation and labeling, and learn about evaluation metrics for deep learning models. Programming examples will be provided in Python or Matlab. The course includes: ●A gentle introduction to digital image analysis and deep learning. ●Convolutional neural networks. ●Transfer learning. ●Image data labeling for deep learning. ●Evaluating deep learning models. ●The challenges of deep learning in the field of digital image analysis. ●Activation functions. ●Loss functions. ●Optimizers. | |||
| 核心能力指標 | B.策劃及執行專題研究之能力。 C.閱讀、撰寫與簡報專業論文之能力。 I.培養科技英文之能力。 | |||
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