深度學習
Deep Learning
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 深度學習 ED203（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 (林胤汶, 馮凡哲, 林祐群, 林境觀, 陳冠綸, 林宥騰) 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
-
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. Variational Auto-Encoders 8. Generative Adversarial Networks 9. Diffusion Models 10. Generative Pre-trained Transformer 11. Large Language Models
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Deep Learning 2025-02-21(五) |
| 第 2 週 | National Holiday 2025-02-28(五) |
| 第 3 週 | Deep Neural Networks 2025-03-07(五) |
| 第 4 週 | Regularization for Deep Learning/Convolutional Neural Networks 2025-03-14(五) |
| 第 5 週 | Optimization for Deep Models (1st Homework) 2025-03-21(五) |
| 第 6 週 | Optimization for Deep Models 2025-03-28(五) |
| 第 7 週 | National Holiday 2025-04-04(五) |
| 第 8 週 | Recurrent Neural Networks (Proposal) 2025-04-11(五) |
| 第 9 週 | Attention Mechanism and Transformer 2025-04-18(五) |
| 第 10 週 | Variational Auto-Encoders (2nd Homework) 2025-04-25(五) |
| 第 11 週 | Generative Adversarial Networks 2025-05-02(五) |
| 第 12 週 | Diffusion Models 2025-05-09(五) |
| 第 13 週 | Large Language Models 2025-05-16(五) |
| 第 14 週 | Project Presentation 2025-05-23(五) |
| 第 15 週 | National Holiday 2025-05-30(五) |
| 第 16 週 | Project Presentation 2025-06-06(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