電機工程系

課程名稱

深度學習數位影像分析

3學分

3小時

英文課程名稱

Deep Learning for Digital Image Analysis

中文課程概要

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

英文課程概要

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.