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, 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) 75%, Paper study & presentation (done in groups of 1-2 members) 10%, Final project 15% and Attendance (for reference)
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction 2024-09-02(一) |
| 第 2 週 | Machine Learning Basics (1/2) 2024-09-09(一) |
| 第 3 週 | Machine Learning Basics (2/2) 2024-09-16(一) |
| 第 4 週 | Deep Feedforward Networks 2024-09-23(一) |
| 第 5 週 | Regularization for Deep Learning 2024-09-30(一) |
| 第 6 週 | Optimization Deep Models for Training 2024-10-07(一) |
| 第 7 週 | The Convelutional Network and its Building Blocks (1/2) 2024-10-14(一) |
| 第 8 週 | The Convelutional Network and its Building Blocks (2/2) 2024-10-21(一) |
| 第 9 週 | Recurrent and Recursive Nets 2024-10-28(一) |
| 第 10 週 | Linear Factor Models 2024-11-04(一) |
| 第 11 週 | Autoencoders 2024-11-11(一) |
| 第 12 週 | Paper Presentations 2024-11-18(一) |
| 第 13 週 | Generative Adversarial Networks 2024-11-25(一) |
| 第 14 週 | Structured Probabilistic Models for Deep Learning 2024-12-02(一) |
| 第 15 週 | Monte Carlo Methods 2024-12-09(一) |
| 第 16 週 | Attention Mechanisms 2024-12-16(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 3:00PM-5:00PM
- 聯絡方式
- jenjee@nycu.edu.tw