遞迴神經網路與變形器
Recurrent Neural Network and Transformer
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 遞迴神經網路與變形器 CM212（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 provides an in-depth introduction to two significant AI models: Recurrent Neural Networks (RNNs) and Transformers. These models play a critical role in handling sequential data and natural language processing tasks. The course begins with an overview of the basic structure and operational principles of RNNs, focusing on how their recurrent architecture learns temporal dependencies. It also delves into how Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) address the long-term dependency issues inherent in RNNs, highlighting their respective advantages. Next, we will explore the fundamental architecture and principles of Transformers, including their core mechanisms such as self-attention and multi-head attention. The course will also introduce the development and applications of Bidirectional Encoder Representations from Transformers (BERT) and related models. The applications of Transformers extend beyond natural language processing to areas like image processing and computer vision. Furthermore, we will examine the development of the latest AI models based on Transformers, with particular focus on their applications in large language models (LLMs), such as the GPT series. Topics include continual domain-adaptive pretraining, low-rank fine-tuning, retrieval-augmented generation, and other techniques. Through practical examples and programming assignments, this course aims to help students understand how to apply these network models to real-world problems. Objectives: Objective 1: Develop students' understanding and ability to apply Recurrent Neural Networks (RNNs, LSTMs, GRUs) for handling time-series data and natural language texts. Objective 2: Equip students with a mastery of Transformers and their applications in natural language processing, image processing, and large language models, as well as familiarity with the latest related technologies. Objective 3: Introduce the applications of Transformers in image processing and computer vision, such as Vision Transformer, SWIN, DETR, MAE, and BEiT.
None
無備註
Instruction and project-based learning. Course material is available on E3 learning platform.
Programming homework.: 60% (4x15%) Paper presentation: 10% Final project: 30%
教師未提供此項資料
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course Introduction RNN 1: Network Architectures 2025-02-18(二) 時數：[2025-02-18]黃仁竑(3.00) |
| 第 2 週 | RNN 2: Learning Processes RNN 3: Recurrent Neural Networks 2025-02-25(二) 時數：[2025-02-25]黃仁竑(3.00) |
| 第 3 週 | RNN 4: LSTM 2025-03-04(二) 時數：[2025-03-04]黃仁竑(3.00) |
| 第 4 週 | RNN 5 : LSTM, Seq-to-seq model, LSTM with Attention 2025-03-11(二) 時數：[2025-03-11]黃仁竑(3.00) |
| 第 5 週 | RNN 6: Gated Recurrent Unit (GRU), Minimal Gated Unit (MGU) 2025-03-18(二) 時數：[2025-03-18]黃仁竑(3.00) |
| 第 6 週 | Transformer 2025-03-25(二) 時數：[2025-03-25]黃仁竑(3.00) |
| 第 7 週 | BERT 2025-04-01(二) 時數：[2025-04-01]黃仁竑(3.00) |
| 第 8 週 | Pretraining a RoBERTa Model from Scratch 2025-04-08(二) 時數：[2025-04-08]黃仁竑(3.00) |
| 第 9 週 | Downstream NLP Tasks with Transformers 2025-04-15(二) 時數：[2025-04-15]黃仁竑(3.00) |
| 第 10 週 | Text Generation with OpenAI GPT models 2025-04-22(二) 時數：[2025-04-22]黃仁竑(3.00) |
| 第 11 週 | Recent Development of Large Language Models 2025-04-29(二) 時數：[2025-04-29]黃仁竑(3.00) |
| 第 12 週 | Recent Development of Computer Vision using Transformers Vision Transformer (ViT), BERT Pre-Training of Image Transformers (BEiT), End-to-End Object Detection with Transformers (DETR), CF-DERT, etc. 2025-05-06(二) 時數：[2025-05-06]黃仁竑(3.00) |
| 第 13 週 | Continue Learning, Fine tune, Retrieval Augmented Generation of LLM 2025-05-13(二) 時數：[2025-05-13]黃仁竑(3.00) |
| 第 14 週 | Paper Presentation 2025-05-20(二) 時數：[2025-05-20]黃仁竑(3.00) |
| 第 15 週 | Paper presentation 2025-05-27(二) 時數：[2025-05-27]黃仁竑(3.00) |
| 第 16 週 | Final project demo 2025-06-03(二) 時數：[2025-06-03]黃仁竑(3.00) |
| 第 17 週 | 2025-06-10(二) |
| 第 18 週 | 2025-06-17(二) |
1. Fathi M. Salem, Recurrent Neural Networks, Springer, 2022. ISBN 978-3-030-89928-8 2. Denis Rothman, Transformers for Natural Language Processing, Packt Publishing, Jan. 2021. ISBN: 9781800565791
- 地點
- ChiMei 303
- 時間
- Tuesday 10:00-12:00AM
- 聯絡方式
- 55729