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

111-2 316689 深度學習 1 3.0 3 ★ 賴冠廷
賴建宏 AI雙聯專班 10 0 技術組，碩一修，3.18 4.9 4.29 5.13 5.28 6.25

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
111-2	316689	深度學習	1	3.0	3	★	賴冠廷賴建宏	AI雙聯專班	10	0	技術組，碩一修，3.18 4.9 4.29 5.13 5.28 6.25

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 2022-12-20 18:44:13

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 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

評量方式與標準 Homework (40%), Midterm and Final Exam (40%), Final Project (15%), Quizzes (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 Website: http://www.aiotlab.org/teaching/dl_app.html

Email: ktlai@ntut.edu.tw

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

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	2022-12-20 18:44:13
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 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
評量方式與標準	Homework (40%), Midterm and Final Exam (40%), Final Project (15%), Quizzes (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	Website: http://www.aiotlab.org/teaching/dl_app.html Email: ktlai@ntut.edu.tw
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