深度學習與實務
Deep Learning and Practice
| 節 | 週二 | 週四 |
|---|---|---|
N 12:20–13:10 | 深度學習與實務 EC114（光復） 3 節連堂 | |
5 13:20–14:10 | ||
6 14:20–15:10 | ||
A 18:30–19:20 | 深度學習與實務 EC114（光復） 3 節連堂 | |
B 19:30–20:20 | ||
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
(1) To understand the maths of deep learning techniques (2) To familiarize with deep learning tools, such as PyTorch, Tensor Flow, etc. (3) To understand the latest developments and applications of deep learning techniques (4) To develop practical working systems
Linear Algebra, Probability Theory, Machine Learning (suggested) NOTE: (1) To submit final projects as academic papers (2) To hold exhibition to showcase final projects (3) To encourage students to participate in various challenges in the fields of computer vision, gaming, data analytics, etc.
無備註
教師未提供此項資料
Labs (done individually) 40%, Paper presentention (done in groups of 3 members) 20% Final project (done in groups of 3 members) 20% Final exam 20%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | 1. Introduction 2. Introduction to Reinforcement Learning 3. Reinforcement Learning for Lightweight Model |
| 第 2 週 | 1. Machine Learning Basics - Linear Algebra - Probability and Information Theory - Numerical Computation 2. Warm-up (Python + PyTorch) |
| 第 3 週 | 1. Deep Networks - Deep Feedforward Networks - Convolutional Networks 2. Valued Based Reinforcement Learning |
| 第 4 週 | 1. Convolutional Networks 2. Back-Propagation (Lab 1) |
| 第 5 週 | 1. Recurrent and Recursive Nets 2. Regularization for Deep Learning 3. 2048 TD (Lab 2) |
| 第 6 週 | 1. Deep Learning Research - Linear Factor Models - Autoencoders 2. Convolutional Nets (Lab 3) |
| 第 7 週 | 1. Autoencoders 2. Generative Adversarial Networks 3. No class(4/5) |
| 第 8 週 | 1. Generative Adversarial Networks 2. Convolutional Nets (Lab 4) |
| 第 9 週 | 1. Structured Probabilistic Models for Deep Learning 2. Recurrent Nets and Variational autoencoders (Lab 5) |
| 第 10 週 | 1. Monte Carlo Method 2. Approximate Inference 3. Policy-based Reinforcement Learning |
| 第 11 週 | 1. Normalizing Flows 2. Graph Convolutional Neural Networks 3. Deep Reinforcement Learning (Lab 6) |
| 第 12 週 | Paper & project proposal presentation |
| 第 13 週 | 1. Paper & project proposal presentation 2. Generative adversarial networks (Lab 7) |
| 第 14 週 | Paper presentation |
| 第 15 週 | Paper presentation |
| 第 16 週 | Paper presentation |
| 第 17 週 | Final Exam |
| 第 18 週 | TBD |
1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st Ed., MIT Press, Dec. 2016 2. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, Nov. 2017
- 地點
- 教師未提供此項資料
- 時間
- 教師未提供此項資料
- 聯絡方式
- 教師未提供此項資料