Course Syllabus

Course Information：

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

Spring Semester
Year 2023 313553 Building Deep Learning Applications 1 3.0 3 ★ Kuan-Ting Lai ECE
IEECSG
GEECS
MSAI 120 1 電子所、AI學程和電資外生所合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2023	313553	Building Deep Learning Applications	1	3.0	3	★	Kuan-Ting Lai	ECE IEECSG GEECS MSAI	120	1	電子所、AI學程和電資外生所合開

Course Plan：

Instructor Kuan-Ting Lai　Office Hours

Email ktlai@ntut.edu.tw

Last Updated 2022-12-20 18:43:22

Course Objective 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.

Course Schedule 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 6. Advanced Keras API 7. NLP & Word Embedding 8. RNN/LSTM and Stock Price Prediction 9. Attention & Transformer, BERT, GPT-3 10. Generative Adversarial Networks (GANs) 11. Object Detection and Segmentation 12. Deep Reinforcement Learning

Evaluation and grading policy Homework (40%), Midterm and Final Exam (40%), Final Project (15%), Quizzes (5%)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
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

The access to curricular consultation Website: http://www.aiotlab.org/teaching/dl_app.html
FB Group: https://www.facebook.com/groups/2231151677158713
Email: ktlai@ntut.edu.tw

The course corresponds to the SDGs

Note
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

Instructor	Kuan-Ting Lai　Office Hours
Email	ktlai@ntut.edu.tw
Last Updated	2022-12-20 18:43:22
Course Objective	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.
Course Schedule	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 6. Advanced Keras API 7. NLP & Word Embedding 8. RNN/LSTM and Stock Price Prediction 9. Attention & Transformer, BERT, GPT-3 10. Generative Adversarial Networks (GANs) 11. Object Detection and Segmentation 12. Deep Reinforcement Learning
Evaluation and grading policy	Homework (40%), Midterm and Final Exam (40%), Final Project (15%), Quizzes (5%)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 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
The access to curricular consultation	Website: http://www.aiotlab.org/teaching/dl_app.html FB Group: https://www.facebook.com/groups/2231151677158713 Email: ktlai@ntut.edu.tw
The course corresponds to the SDGs
Note	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