深度學習
Deep Learning
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 深度學習 ED103（光復） 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstraction from data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Various deep learning architectures such as deep neural networks, convolutional neural networks, recurrent neural networks, and transformers have been applied to the fields like computer vision, automatic speech recognition, natural language processing, data mining and bioinformatics. State-of-the-art results on various tasks have been successfully developed. This course focuses on the fundamentals and advances in deep learning, in particular generative pre-trained language model in the era of generative artificial intelligence.
Calculus, Linear Algebra, Probability & Statistics
無備註
Teaching notes or slides will be provided. Teacher assistants (黃伯鈞, 陳柏全, 劉品彥, 游翔竣, 葉家愷) will be available at PM19:00-20:00 in week days. Appointments are required. You are encouraged to use online discussion function in E3. TAs will promptly reply your questions.
Temporary Policy: Homework (or Task Competition) (60%), Final Project (40%), Class Attendance (+10%)
-
1. Machine Learning Basics 2. Deep Feedforward Networks 3. Regularization for Deep Learning 4. Convolutional and Recurrent Neural Networks 5. Optimization for Deep Models 6. Transformers and BERT 7. Auto-Encoders and Approximate Inference 8. Variational Auto-Encoders 9. Generative Adversarial Networks 10. Domain Adaptation 11. Generative Models 12. Pre-Trained Language Models 13. Chat Generative Pre-trained Transformer 14. Multi-Modality GPT
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Deep Learning 2024-02-23(五) |
| 第 2 週 | Deep Neural Networks 2024-03-01(五) |
| 第 3 週 | Regularization for Deep Learning/Convolutional Neural Networks 2024-03-08(五) |
| 第 4 週 | No Classes 2024-03-15(五) |
| 第 5 週 | Optimization for Deep Models (1st Homework) 2024-03-22(五) |
| 第 6 週 | Optimization for Deep Models 2024-03-29(五) |
| 第 7 週 | National Holiday 2024-04-05(五) |
| 第 8 週 | Recurrent Neural Networks (Proposal) 2024-04-12(五) |
| 第 9 週 | No Classes 2024-04-19(五) |
| 第 10 週 | Attention Mechanism and Transformer 2024-04-26(五) |
| 第 11 週 | Variational Auto-Encoders (2nd Homework) 2024-05-03(五) |
| 第 12 週 | Generative Adversarial Networks 2024-05-10(五) |
| 第 13 週 | Generative Models 2024-05-17(五) |
| 第 14 週 | Learning with Pre-Trained Models, ChatGPT 2024-05-24(五) |
| 第 15 週 | Project Presentation 2024-05-31(五) |
| 第 16 週 | Project Presentation 2024-06-07(五) |
| 第 17 週 | Project Presentation 2024-06-14(五) |
| 第 18 週 | Supplement Teaching 2024-06-21(五) |
1. I. Goodfellow and Y. Bengio and A. Courville, Deep Learning, The MIT Press, 2016 (http://www.deeplearningbook.org) 2. S. Watanabe and J.-T. Chien, Bayesian Speech and Language Processing, Cambridge University Press, 2015. 3. J.-T. Chien, Source Separation and Machine Learning, Academic Press, 2018. 4. M.-W. Mak and J.-T. Chien, Machine Learning for Speaker Recognition, Cambridge University Press, 2020.
- 地點
- ED 708
- 時間
- PM18:00-18:30 on Monday. Appointments are required.
- 聯絡方式
- jtchien@nycu.edu.tw