Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2025 353100 Deep Learning 1 3.0 3 ★ Yibeltal Chanie Manie 4EOE3 40 0 計中電腦教室,改時間

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	353100	Deep Learning	1	3.0	3	★	Yibeltal Chanie Manie	4EOE3	40	0	計中電腦教室,改時間

Course Plan：

Instructor Yibeltal Chanie Manie　Office Hours

Email yibeltal_chanie@mail.ntut.edu.tw

Last Updated 2025-06-04 12:52:17

Course Objective 1. 深度學習簡介、2.神經網路基礎、3.訓練深度神經網路、4.深度學習架構介紹、 5.卷積神經網路、6. 循環神經網路與序列建模、7. 模型評估與性能指標、8.應用案例分析。 1. Introduction to Deep Learning 2. Fundamentals of Neural Networks 3. Training Deep Neural Networks 4. Introduction to Deep Learning Frameworks 5. Convolutional Neural Networks 6. Recurrent Neural Networks (RNNs) and Sequence Modeling 7. Model Evaluation and Performance Metrics 8. Application Case Studies

Course Schedule Week 1-2. Introduction to Deep Learning Week 3-4. Fundamentals of Neural Networks Week 4-6. Deep Neural Networks Week 7-8. Optimization Techniques Week 9-11. Convolutional Neural Networks Week 12-13. Recurrent Neural Networks (RNNs) and Sequence Modeling Week 14. Reinforcement Learning Intro Week 15-16. Application Case Studies Week 17-18. Final project presentation

Evaluation and grading policy Quizzes & Assignments : 40 % Labs / Mini Projects: 20 % Attendance : 10 % Final Project : 30 %

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
References : 1. Goodfellow, I., Bengio, Y. & Courville, A.: Deep Learning. 2016, MIT. 2. Anirudh Koul, Siddha Ganju, Meher Kasam: Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow 1st Edition

The access to curricular consultation Email: yibeltal_chanie@mail.ntut.edu.tw

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG10：減少不平等（Reduced Inequalities）

Does the course incorporate AI AI1：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）
AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
AI3：使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
AI4：簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
AI5：深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Yibeltal Chanie Manie　Office Hours
Email	yibeltal_chanie@mail.ntut.edu.tw
Last Updated	2025-06-04 12:52:17
Course Objective	1. 深度學習簡介、2.神經網路基礎、3.訓練深度神經網路、4.深度學習架構介紹、 5.卷積神經網路、6. 循環神經網路與序列建模、7. 模型評估與性能指標、8.應用案例分析。 1. Introduction to Deep Learning 2. Fundamentals of Neural Networks 3. Training Deep Neural Networks 4. Introduction to Deep Learning Frameworks 5. Convolutional Neural Networks 6. Recurrent Neural Networks (RNNs) and Sequence Modeling 7. Model Evaluation and Performance Metrics 8. Application Case Studies
Course Schedule	Week 1-2. Introduction to Deep Learning Week 3-4. Fundamentals of Neural Networks Week 4-6. Deep Neural Networks Week 7-8. Optimization Techniques Week 9-11. Convolutional Neural Networks Week 12-13. Recurrent Neural Networks (RNNs) and Sequence Modeling Week 14. Reinforcement Learning Intro Week 15-16. Application Case Studies Week 17-18. Final project presentation
Evaluation and grading policy	Quizzes & Assignments : 40 % Labs / Mini Projects: 20 % Attendance : 10 % Final Project : 30 %
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes References : 1. Goodfellow, I., Bengio, Y. & Courville, A.: Deep Learning. 2016, MIT. 2. Anirudh Koul, Siddha Ganju, Meher Kasam: Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow 1st Edition
The access to curricular consultation	Email: yibeltal_chanie@mail.ntut.edu.tw
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG10：減少不平等（Reduced Inequalities）
Does the course incorporate AI	AI1：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） AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） AI3：使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） AI4：簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） AI5：深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note