深度學習
Deep Learning
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 深度學習 ED103 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
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%)
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Deep Learning |
| 第 2 週 | Deep Neural Networks |
| 第 3 週 | Regularization for Deep Learning/Convolutional Neural Networks |
| 第 4 週 | No Classes |
| 第 5 週 | Optimization for Deep Models (1st Homework) |
| 第 6 週 | Optimization for Deep Models |
| 第 7 週 | National Holiday |
| 第 8 週 | Recurrent Neural Networks (Proposal) |
| 第 9 週 | No Classes |
| 第 10 週 | Attention Mechanism and Transformer |
| 第 11 週 | Variational Auto-Encoders (2nd Homework) |
| 第 12 週 | Generative Adversarial Networks |
| 第 13 週 | Generative Models |
| 第 14 週 | Learning with Pre-Trained Models, ChatGPT |
| 第 15 週 | Project Presentation |
| 第 16 週 | Project Presentation |
| 第 17 週 | Project Presentation |
| 第 18 週 | Supplement Teaching |
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