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

112-2 328215 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
人工智慧學位學程
創新AI學位學程 65 0 電子所、電資、創新AI和電資外生所合開

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
112-2	328215	深度學習應用開發實務	1	3.0	3	★	賴冠廷	電子所電資外生所人工智慧學位學程創新AI學位學程	65	0	電子所、電資、創新AI和電資外生所合開

教學大綱與進度：

教師姓名賴冠廷　教師諮商時間(Office Hours)

Email ktlai@ntut.edu.tw

最後更新時間 2024-05-16 11:07:31

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
(1-16週) 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. Foundation Models, ChatGPT, and Hugging Face 12. Object Detection and Segmentation 13. Generative Adversarial Networks (GAN) 14. Diffusion is all you need? Stable diffusion, midjourney, DALL-E 15. Deep Reinforcement Learning and Stable Baseline 16. Graph Convolution Networks (GCN) 17. Final project demo 18. Final exam

評量方式與標準 Homework (40%), Midterm and Final Exam (35%), Final Project (15%), Quizzes and attendance (10%)

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

NOTE: About week 17 & 18, we will have online final project presentation. Please upload your final project videos to YouTube.

延伸教學與資源

課程對應SDGs指標

課程是否導入AI

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　教師諮商時間(Office Hours)
Email	ktlai@ntut.edu.tw
最後更新時間	2024-05-16 11:07:31
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 (1-16週)	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. Foundation Models, ChatGPT, and Hugging Face 12. Object Detection and Segmentation 13. Generative Adversarial Networks (GAN) 14. Diffusion is all you need? Stable diffusion, midjourney, DALL-E 15. Deep Reinforcement Learning and Stable Baseline 16. Graph Convolution Networks (GCN) 17. Final project demo 18. Final exam
評量方式與標準	Homework (40%), Midterm and Final Exam (35%), Final Project (15%), Quizzes and attendance (10%)
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 NOTE: About week 17 & 18, we will have online final project presentation. Please upload your final project videos to YouTube.
延伸教學與資源
課程對應SDGs指標
課程是否導入AI
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