深度學習
Deep Learning
| 節 | 週一 |
|---|---|
5 13:20–14:10 | 深度學習 CM217（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, regularization, RNNs, Autoencoders, GANs, attention mechanisms ... 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 and quiz (done individually) 70%, Paper study & presentation (done in groups of 1-2 members) 10%, Final project 20% and Attendance (for reference)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction 2026-09-07(一) |
| 第 2 週 | Machine Learning Basics (1/2) 2026-09-14(一) |
| 第 3 週 | Machine Learning Basics (2/2) 2026-09-21(一) |
| 第 4 週 | 教師節放假一天 2026-09-28(一) |
| 第 5 週 | Deep Feedforward Networks 2026-10-05(一) |
| 第 6 週 | Regularization for Deep Learning 2026-10-12(一) |
| 第 7 週 | Optimization Deep Models for Training 2026-10-19(一) |
| 第 8 週 | 光復節補假放假一天 2026-10-26(一) |
| 第 9 週 | The Convelutional Network and its Building Blocks 2026-11-02(一) |
| 第 10 週 | Recurrent and Recursive Nets 2026-11-09(一) |
| 第 11 週 | Linear Factor Models 2026-11-16(一) |
| 第 12 週 | Paper Presentations 2026-11-23(一) |
| 第 13 週 | Autoencoders 2026-11-30(一) |
| 第 14 週 | Generative Adversarial Networks 2026-12-07(一) |
| 第 15 週 | Monte Carlo Methods 2026-12-14(一) |
| 第 16 週 | Attention Mechanisms; Final Project Presentation 2026-12-21(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 3. Related Publications
- 地點
- My office
- 時間
- Tuesday 2:00PM-4:00PM
- 聯絡方式
- jenjee@nycu.edu.tw