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 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 (黃聖哲、楊舒翔、張惟翔、陳奕翔、徐傳恩、羅天進) will be available at PM19:30-20:30 in week days. Due to the pandemic of COVID-19, please use online discussion function in E3. TAs will promptly reply your questions.
Temporary Policy: Final Exam or Task Competition (35%), Homework (40%), Final Project (25%), 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 8. Auto-Encoders and 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 March 6 |
| 第 2 週 | Deep Feedforward Networks March 13 |
| 第 3 週 | Regularization for Deep Learning/Tutorial for Pytorch and GPU Server March 20 |
| 第 4 週 | Convolutional Neural Networks March 27 (1st Homework) |
| 第 5 週 | National Holiday April 3 |
| 第 6 週 | Optimization for Deep Models April 10 |
| 第 7 週 | Optimization for Deep Models April 17 |
| 第 8 週 | Recurrent Neural Networks April 24 (Proposal) |
| 第 9 週 | Memory Networks and Attention Mechanism May 1 (2nd Homework) |
| 第 10 週 | Auto-Encoders and Approximate Inference May 8 |
| 第 11 週 | Variational Auto-Encoders May 15 |
| 第 12 週 | Generative Adversarial Networks May 22 |
| 第 13 週 | Stochastic Modeling and Learning & Reinforcement Learning May 29 (3rd Homework) |
| 第 14 週 | Generative Adversarial Network (Advanced topics) June 5 |
| 第 15 週 | Reinforcement Learning (Advanced Topics) June 12 |
| 第 16 週 | Project Presentation (ED219) June 19 |
| 第 17 週 | National Holiday & Task competition (June 20-25) June 26 |
| 第 18 週 | July 3 |
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, October 2018.
- 地點
- ED 912
- 時間
- PM17:00-18:00 on Monday
- 聯絡方式
- jtchien@nctu.edu.tw