深度學習
Deep Learning
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 深度學習 CM216（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 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
無備註
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)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction 2023-09-11(一) |
| 第 2 週 | Machine Learning Basics (1/2) 2023-09-18(一) |
| 第 3 週 | Machine Learning Basics (2/2) 2023-09-25(一) |
| 第 4 週 | Deep Feedforward Networks 2023-10-02(一) |
| 第 5 週 | Holiday 2023-10-09(一) |
| 第 6 週 | Regularization for Deep Learning 2023-10-16(一) |
| 第 7 週 | Optimization Deep Models for Training 2023-10-23(一) |
| 第 8 週 | Convelutional Networks 2023-10-30(一) |
| 第 9 週 | Recurrent and Recursive Nets 2023-11-06(一) |
| 第 10 週 | Presentations 2023-11-13(一) |
| 第 11 週 | Linear Factor Models 2023-11-20(一) |
| 第 12 週 | Autoencoders 2023-11-27(一) |
| 第 13 週 | Generative Adversarial Networks 2023-12-04(一) |
| 第 14 週 | Structured Probabilistic Models for Deep Learning 2023-12-11(一) |
| 第 15 週 | Deep Generative Models 2023-12-18(一) |
| 第 16 週 | Monte Carlo Methods 2023-12-25(一) |
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