查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

115-1 366054 深度學習應用開發實務 1 3.0 3 ★ 賴冠廷電子所
電資外生所
創新AI學位學程 4 0 電子所、電資外生所和創新AI合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
115-1	366054	深度學習應用開發實務	1	3.0	3	★	賴冠廷	電子所電資外生所創新AI學位學程	4	0	電子所、電資外生所和創新AI合開

教學大綱與進度：

教師姓名賴冠廷

Email ktlai@ntut.edu.tw

最後更新時間 2026-06-04 22:17:56

課程大綱 The goal of this course is to give students a broad overview of state-of-the-art deep learning algorithms, and teach students how to develop deep learning applications by hands-on coding examples. Most examples taught in this class are based on the TensorFlow framework. We will cover many different fields including image classification, object detection, natural language processing, reinforcement learning and generative adversarial networks.

課程進度 Course website: http://www.aiotlab.org/teaching/dl_app.html 1. Introduction to Deep Learning 2. Applied Math (Linear Algebra, Probability, Calculus) 3. Machine Learning Basics 4. TensorFlow & Keras 5. Convolutional Neural Networks (CNN) 6. Advanced Keras API & PyTorch 7. Natural Language Processing (NLP), Word Embedding, Self-supervised Learning 8. Midterm exam 9. Recurrent Neural Networks (RNN) & Long Short-term Memory (LSTM) 10. Attention is all you need! Attention and Transformer 11. Large Language Models (LLMs), ChatGPT, Gemini, DeepSeek 12. Agentic AI: OpenClaw, Hermes Agent, and more 13. Object Detection and Segmentation 14. Generative Adversarial Networks (GAN) and Diffusion models 15. Deep Reinforcement Learning and Stable Baseline 16. Graph Convolution Networks (GCNs) 17. Group Study and Presentation 18. Final exam

評量方式與標準 Homework (30%), Midterm (20%), Final Exam (20%), Group Study (10%), Quizzes (15%) and attendance (5%)

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：是
1. Francois Chollet, “Deep Learning with Python, 2nd edition” Manning, 2021 2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning,” MIT Press, 2017 3. Latest publications on CVPR, NIPS, ICML, AAAI, ICLR Website: http://www.aiotlab.org/teaching/dl_app.html

課程諮詢管道 FB Group: https://www.facebook.com/groups/2231151677158713
Email: ktlai@ntut.edu.tw

延伸教學與資源 ● 數位科技融入 (Digital Technology Integration)
運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。
Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment.
● 數位教材資源應用 (Application of Digital Resources)
採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。
Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
● 參與式創新教學 (Participatory & Innovative Teaching)
強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。
Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.

課程對應SDGs指標 SDG3：良好健康和福祉（Good Health and Well-being）
SDG4：優質教育（Quality Education）
SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth）
SDG10：減少不平等（Reduced Inequalities）

課程是否導入AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

備註 All quizzes, exams and homework will be made online using Microsoft Teams. For final project, students will need to submit a YouTube video. Any announcements will be posted in Microsoft Teams. Website: http://www.aiotlab.org/teaching/dl_app.html FB Group: https://www.facebook.com/groups/2231151677158713

教師姓名	賴冠廷
Email	ktlai@ntut.edu.tw
最後更新時間	2026-06-04 22:17:56
課程大綱	The goal of this course is to give students a broad overview of state-of-the-art deep learning algorithms, and teach students how to develop deep learning applications by hands-on coding examples. Most examples taught in this class are based on the TensorFlow framework. We will cover many different fields including image classification, object detection, natural language processing, reinforcement learning and generative adversarial networks.
課程進度	Course website: http://www.aiotlab.org/teaching/dl_app.html 1. Introduction to Deep Learning 2. Applied Math (Linear Algebra, Probability, Calculus) 3. Machine Learning Basics 4. TensorFlow & Keras 5. Convolutional Neural Networks (CNN) 6. Advanced Keras API & PyTorch 7. Natural Language Processing (NLP), Word Embedding, Self-supervised Learning 8. Midterm exam 9. Recurrent Neural Networks (RNN) & Long Short-term Memory (LSTM) 10. Attention is all you need! Attention and Transformer 11. Large Language Models (LLMs), ChatGPT, Gemini, DeepSeek 12. Agentic AI: OpenClaw, Hermes Agent, and more 13. Object Detection and Segmentation 14. Generative Adversarial Networks (GAN) and Diffusion models 15. Deep Reinforcement Learning and Stable Baseline 16. Graph Convolution Networks (GCNs) 17. Group Study and Presentation 18. Final exam
評量方式與標準	Homework (30%), Midterm (20%), Final Exam (20%), Group Study (10%), Quizzes (15%) and attendance (5%)
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：是 1. Francois Chollet, “Deep Learning with Python, 2nd edition” Manning, 2021 2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning,” MIT Press, 2017 3. Latest publications on CVPR, NIPS, ICML, AAAI, ICLR Website: http://www.aiotlab.org/teaching/dl_app.html
課程諮詢管道	FB Group: https://www.facebook.com/groups/2231151677158713 Email: ktlai@ntut.edu.tw
延伸教學與資源	● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment. ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials. ● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.
課程對應SDGs指標	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG10：減少不平等（Reduced Inequalities）
課程是否導入AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
備註	All quizzes, exams and homework will be made online using Microsoft Teams. For final project, students will need to submit a YouTube video. Any announcements will be posted in Microsoft Teams. Website: http://www.aiotlab.org/teaching/dl_app.html FB Group: https://www.facebook.com/groups/2231151677158713