深度學習
Deep Learning
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 深度學習 ED219（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 abstractions in 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 deep neural networks, deep belief networks and recurrent neural networks 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.
Calculus, Linear Algebra, Probability & Statistics
無備註
Teaching notes or slides will be provided. Teacher assistants (楊立任, 王心玓, 葉宜萍, 徐靖憲) 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%)
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1. Machine Learning Basics 2. Deep Learning Applications 3. Deep Feedforward Networks 4. Regularization for Deep Learning 5. Convolutional Neural Networks 6. Optimization for Deep Models 7. Recurrent Neural Networks/ Transformers 8. Auto-Encoders and Approximate Inference 9. Variational Auto-Encoders 10. Generative Adversarial Networks 11. Domain Adaptation 12. Generative Models 13. Learning with Pre-Trained Models
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Deep Learning 2023-02-17(五) |
| 第 2 週 | Deep Neural Networks 2023-02-24(五) |
| 第 3 週 | Forum: Multi-Modal Foundation Model/Tutorial for Pytorch and GPU Server 2023-03-03(五) |
| 第 4 週 | Regularization for Deep Learning/Convolutional Neural Networks 2023-03-10(五) |
| 第 5 週 | Optimization for Deep Models 2023-03-17(五) |
| 第 6 週 | Optimization for Deep Models (1st Homework) 2023-03-24(五) |
| 第 7 週 | Recurrent Neural Networks and Memory Networks 2023-03-31(五) |
| 第 8 週 | Attention Mechanism and Transformer 2023-04-07(五) |
| 第 9 週 | Auto-Encoders and Approximate Inference (Proposal) 2023-04-14(五) |
| 第 10 週 | Variational Auto-Encoders 2023-04-21(五) |
| 第 11 週 | Generative Adversarial Networks 2023-04-28(五) |
| 第 12 週 | Transfer Learning (2nd Homework) 2023-05-05(五) |
| 第 13 週 | Generative Models 2023-05-12(五) |
| 第 14 週 | Learning with Pre-Trained Models 2023-05-19(五) |
| 第 15 週 | Project Presentation 2023-05-26(五) |
| 第 16 週 | Project Presentation 2023-06-02(五) |
| 第 17 週 | Supplement Teaching 2023-06-09(五) |
| 第 18 週 | Supplement Teaching 2023-06-16(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