深度學習與實務
Deep Learning and Practice
| 節 | 週二 | 週四 |
|---|---|---|
N 12:20–13:10 | 深度學習與實務 EC114 3 節連堂 | |
5 13:20–14:10 | ||
6 14:20–15:10 | ||
A 18:30–19:20 | 深度學習與實務 EC114 3 節連堂 | |
B 19:30–20:20 | ||
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
(1) To understand the maths of deep learning techniques (2) To familiarize with deep learning tools, such as PyTorch, Tensor Flow, etc. (3) To understand the latest developments and applications of deep learning techniques (4) To develop practical working systems
Linear Algebra, Probability Theory, Machine Learning (suggested) 2XEF-EC114 (for lectures) 4IJK-EC114 (for Lab) NOTE: (1) The first lecture will begin on Feb. 19 (2XEF-EC114). (2) If you want to enroll in this course, you need to be present on Feb. 19 (2XEF-EC114) and submit your enrollment form in person (if you have not yet been enrolled successfully). (3) Be advised that if we have more students taking this course than we could afford, your final enrollment will be subject to review by all the instructors. (4) More details will be announced during the first lecture. Make sure that you don't miss it.
Labs (done individually) 40%, Paper presentention (done in groups of 2 members) 20% Final project (done in groups of 2 members) 20% Final exam 20%
| 週次 | 主題 |
|---|---|
| 第 1 週 | A. Introduction |
| 第 2 週 | B. Machine Learning Basics |
| 第 3 週 | C. Deep Networks |
| 第 4 週 | Convolutional Networks |
| 第 5 週 | Optimization for Training Deep Models Recurrent and Recursive Nets |
| 第 6 週 | Regularization for Deep Learning |
| 第 7 週 | D. Deep Learning Research |
| 第 8 週 | Autoencoders Generative Adversarial Networks |
| 第 9 週 | Generative Adversarial Networks Structured Probabilistic Models for Deep Learning |
| 第 10 週 | Approximate Inference Restricted Boltzmann Machines |
| 第 11 週 | E. Deep Reinforcement Learning |
| 第 12 週 | Final project & Paper proposal presentation |
| 第 13 週 | Monte-Carlo Learning +Policy Gradient |
| 第 14 週 | Various DRL Methods. |
| 第 15 週 | F. Paper Study and Presentation |
| 第 16 週 | G. Paper Study and Presentation |
| 第 17 週 | H. Final Exam |
| 第 18 週 | I. Final Project Presentation (TBD) |
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st Ed., MIT Press, Dec. 2016 2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Nov. 2017