課 程 概 述
Course Description

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
The field of digital image analysis is growing rapidly in many application areas such as manufacturing, medicine, agriculture, transportation, and communication systems. This course aims to introduce the foundations of deep learning for digital image analysis from the fundamental theoretical concepts to applications. The class will provide students with an opportunity to explore the state-of-the-art deep learning models for object detection, image classification, and image segmentation with practical hands-on experience. The challenges of deep learning in the field of digital image analysis will be discussed in this class. The course will also explore some popular image datasets such as MNIST, CIFAR-10, and ImageNet, covering the image data preparation and labeling for deep learning. Evaluation metrics for deep learning models will be examined in detail. Students are free to use any programming language, however the programming examples in this course will be given in Matlab and Python

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  1. 本資料係由本校各教學單位、教務處課務組、進修部教務組、進修學院教務組及計網中心所共同提供!
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