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 fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.
Calculus, Linear Algebra, Probability & Statistics
無備註
Teaching notes or slides will be provided. Teacher assistants (陳幽秀、蔡知融、黃煜閔、邱奕中、許伯阡) will be available at PM19:30-20:30 in ED 912 in week days.
Temporary Policy: Final Exam (40%), Homework (40%), Final Project (20%), 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. Optimization for Deep Models 6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Auto-Encoders & Approximate Inference 9. Variational Auto-Encoders 10. Generative Adversarial Networks 11. Deep Domain Adaptation 12. Reinforcement Learning
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Deep Learning |
| 第 2 週 | Deep Feedforward Networks National Holiday |
| 第 3 週 | Regularization for Deep Learning |
| 第 4 週 | Tutorial for Pytorch & GPU Server |
| 第 5 週 | Optimization for Deep Models 1st Homework |
| 第 6 週 | Optimization for Deep Models |
| 第 7 週 | Convolutional Neural Networks National Holiday |
| 第 8 週 | Recurrent Neural Networks |
| 第 9 週 | Memory Networks Proposal |
| 第 10 週 | Attention Mechanism 2nd Homework |
| 第 11 週 | Auto-Encoders & Approximate Inference |
| 第 12 週 | Variational Auto-Encoders |
| 第 13 週 | Introduction to Final Project/Generative Adversarial Networks ICASSP |
| 第 14 週 | Generative Adversarial Networks 3rd Homework |
| 第 15 週 | Deep Domain Adaptation |
| 第 16 週 | Reinforcement Learning National Holiday |
| 第 17 週 | Final Exam |
| 第 18 週 | Project Presentation |
1. I. Goodfellow and Y. Bengio and A. Courville, Deep Learning, The MIT Press, 2016 (http://www.deeplearningbook.org) 2. J.-T. Chien, Source Separation and Machine Learning, Academic Press, October 2018. 3. S. Watanabe and J.-T. Chien, Bayesian Speech and Language Processing, Cambridge University Press, 2015.
- 地點
- ED 912
- 時間
- PM17:00-18:00 on Thursday
- 聯絡方式
- jtchien@nctu.edu.tw