深度學習
Deep Learning
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 深度學習 CM216 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 is to help students enter the field of deep learning. We will begin by studying the fundamental math theories which is needed in deep learning. Then, the theories of various neural network architectures and building blocks, including convolutional networks, gradient descent based optimizers, ... etc., will be introduced. We will also explore some use cases of deep learning.
Linear Algebra, Probability, Programming Language
Lectures, labs, experiments, and projects
Temporary Policy: Labs, homework and quiz (done individually) 70%, Paper presentation (done in groups of 1-2 members) 30% and Attendance (for reference)
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | Machine Learning Basics (1/2) |
| 第 3 週 | Machine Learning Basics (2/2) |
| 第 4 週 | Deep Feedforward Networks |
| 第 5 週 | Holiday |
| 第 6 週 | Regularization for Deep Learning |
| 第 7 週 | Optimization Deep Models for Training |
| 第 8 週 | Convelutional Networks |
| 第 9 週 | Recurrent and Recursive Nets |
| 第 10 週 | Presentations |
| 第 11 週 | Linear Factor Models |
| 第 12 週 | Autoencoders |
| 第 13 週 | Generative Adversarial Networks |
| 第 14 週 | Structured Probabilistic Models for Deep Learning |
| 第 15 週 | Deep Generative Models |
| 第 16 週 | Monte Carlo Methods |
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, The MIT Press, 2016 2. François Chollet, Deep Learning with Python, Manning Publications, 2017
- 地點
- My office
- 時間
- Tuesday 9:00AM-11:00AM
- 聯絡方式
- jenjee@nycu.edu.tw