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
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Teaching notes or slides will be provided. Teacher assistants (湯宗哲、劉冠汶、陳遠安、陳明彥、孫維佑、金華晟) 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: 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. Basics in Reinforcement Learning 13. Advances in Reinforcement Learning
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Deep Learning Feb 18 |
| 第 2 週 | Deep Neural Networks Feb 25 |
| 第 3 週 | Regularization for Deep Learning/Tutorial for Pytorch and GPU Server March 4 |
| 第 4 週 | Convolutional Neural Networks March 11 (1st Homework, DNN, CNN) |
| 第 5 週 | Optimization for Deep Models March 18 |
| 第 6 週 | Optimization for Deep Models March 25 |
| 第 7 週 | Recurrent Neural Networks and Memory Networks April 1 |
| 第 8 週 | Attention Mechanism and Transformer April 8 (Proposal) |
| 第 9 週 | Auto-Encoders and Approximate Inference April 15 (2nd Homework, RNN, Transformer, VAE) |
| 第 10 週 | Variational Auto-Encoders April 22 |
| 第 11 週 | Generative Adversarial Networks April 29 |
| 第 12 週 | Transfer Learning May 6 |
| 第 13 週 | Basics in Reinforcement Learning May 13 (3rd Homework, GAN, DQN) |
| 第 14 週 | Advances in Reinforcement Learning May 20 |
| 第 15 週 | Project Presentation (ED219) May 27 |
| 第 16 週 | National Holiday June 3 |
| 第 17 週 | Project Presentation (ED219) June 10 |
| 第 18 週 | Project Presentation (ED219) June 17 |
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@nycu.edu.tw