深度學習
Deep Learning
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 深度學習 CM218（歸仁） 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
This course intended to help students enter the field of deep learning. We will study the fundamental theory of various neural network architectures and building blocks, including convolutional networks, gradient descent based optimizers and math behind them. Then we will explore a few use cases of deep learning, including generative models and reinforcement learning algorithms.
Linear algebra, Multivariable Calculus, Programming(mostly in Python)
無備註
Lectures and various experiments and projects as coursework.
100% Coursework(projects, experiments)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Overview and Enviromen Setup 9/9 |
| 第 2 週 | Math and Machine Learning Basics 9/16 |
| 第 3 週 | Deep Feedforward Networks 9/23 |
| 第 4 週 | Regularization and Training 9/30 |
| 第 5 週 | Covolutional Networks Basic 10/7 |
| 第 6 週 | Applications and Reviews 10/14 |
| 第 7 週 | Recurrent Nets 10/21 |
| 第 8 週 | Midterm Project. In class competition 10/28 |
| 第 9 週 | Some Modern Network Building Blocks 11/4 |
| 第 10 週 | Linear Factor Models 11/11 |
| 第 11 週 | Autoencoders 11/18 |
| 第 12 週 | Structured Probabilistic Models for Deep Learning 11/25 |
| 第 13 週 | Deep Generative Models 12/2 |
| 第 14 週 | Topics and Applications 12/9 |
| 第 15 週 | Monte Carlo Methods 12/16 |
| 第 16 週 | Introduction to Deep Reinforcement learning 12/23 |
| 第 17 週 | Topics and review 12/30 |
| 第 18 週 | The Final Lecture 1/6 |
Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 https://www.deeplearningbook.org/
- 地點
- My Office or by reservation
- 時間
- Tuesday 8:00-10:00
- 聯絡方式
- tjw@nctu.edu.tw