| 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 |
|---|---|---|---|---|
| 3604164 | 深度學習 | Deep Learning | 3.0 | 3 |
| 中文概述 Chinese Description | 課程主要為介紹深度學習的理論基礎, 和目前學術界最新的研究成果. , 並教導學員使用Python深度學習套件，讓學員能將深度學習技術應用於各個不同的領域。課程內容包括: 1.深度學習簡介 2.機器學習基礎理論 3.反向傳播與梯度下降演算法 4.卷積神經網路(CNN) 5.影像辨識與物件偵測 6.遞迴式神經網路(RNN & LSTM) 7.對抗生成網路 (GAN) 8.非監督式深度學習(Unsuprivsed Deep Learning) 9.深度強化學習 (Deep Reinforcement Learning) | |||
| 英文概述 English Description | The goal of this course is to introduce fundamental theories behind deep learning and summarize the state-of-the-art research results. We will also teach how to use Python library (TensorFlow & Keras) and demonstrate how to apply deep learning algorithms to real-life applications. The content includes: 1.Introduction to Deep Learning 2.Machine Learning Basics 3.Backpropagation and Gradient Descent 4.Convolutional Neural Networks (CNN) 5.Image Classification and Object Detection 6.Recursive Neural Networks (RNN) and Long Short-Term Memory (LSTM) 7.Generative Adversarial Networks 8.Unsuprivsed Deep Learning 9.Deep Reinforcement Learning | |||
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