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 (朱長亭、張哲瑋、田修瑋、黃毓涵、張譽瀚、賴偉偉) will be available at PM19:00-20:00 in week days. Appointments are required. Due to the pandemic of COVID-19, you are encouraged to use online discussion function in E3. TAs will promptly reply your questions.
Temporary Policy: Final Exam or Task Competition (25%), Homework (40%), Final Project (35%), 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. Deep Domain Adaptation 12. Reinforcement Learning 13. Self Supervised Learning
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Deep Learning Feb 26 |
| 第 2 週 | Deep Feedforward Networks March 5 |
| 第 3 週 | Regularization for Deep Learning/Tutorial for Pytorch and GPU Server March 12 |
| 第 4 週 | Convolutional Neural Networks March 19 (1st Homework, DNN, CNN) |
| 第 5 週 | Optimization for Deep Models March 26 |
| 第 6 週 | National Holiday April 2 |
| 第 7 週 | Optimization for Deep Models April 9 |
| 第 8 週 | Recurrent Neural Networks and Memory Networks April 16 (Proposal) |
| 第 9 週 | Attention Mechanism and Transformer April 23 (2nd Homework, RNN, Transformer, VAE) |
| 第 10 週 | Auto-Encoders and Approximate Inference April 30 |
| 第 11 週 | Variational Auto-Encoders May 7 |
| 第 12 週 | Generative Adversarial Networks May 14 |
| 第 13 週 | Stochastic Modeling & Reinforcement Learning May 21 (3rd Homework, GAN, DQN) |
| 第 14 週 | Reinforcement Learning May 28 |
| 第 15 週 | Self Supervised Learning June 4 |
| 第 16 週 | Project Presentation (ED219) June 11 |
| 第 17 週 | Project Presentation (ED219) June 18 |
| 第 18 週 | Task competition (June 20-25) June 25 |
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 912
- 時間
- PM18:00-18:30 on Monday. Appointments are required.
- 聯絡方式
- jtchien@nctu.edu.tw