大型語言模型
Large Language Models
| 節 | 週五 |
|---|---|
5 13:20–14:10 | 大型語言模型 ED103 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
Large language models (LLMs) offers a unique opportunity to peek behind the curtains of the groundbreaking advancements in generative artificial intelligence (GAI). By delving into these developments, you will understand how language models are evolved and exploited to deploy over various domains. This course will address how LLMs work under the hood, tearing the lid off the GAI black box. LLMs provide a platform to explore how machines can comprehend, generate, and manipulate language in ways that mimic human cognition. This course focuses on the fundamentals in computation theories, architectures and practices for LLMs, and highlights the academic and industrial advances in the extended models and applications.
Calculus, Linear Algebra, Probability and Statistics
Teaching materials, codes and datasets will be provided. Teacher assistants will be available at PM19:00-20:00 in week days. Appointments are required. You are encouraged to use online discussion function in E3. TAs will promptly reply your questions.
Temporary Policy: Homework (or Task Competition) (60%), Final Project (40%), Class Attendance (+10%)
| 週次 | 主題 |
|---|---|
| 第 1 週 | Feedforward & Convolutional Neural Networks |
| 第 2 週 | Regularization for Optimization in Deep Learning |
| 第 3 週 | Recurrent Neural Network & Sequential Learning |
| 第 4 週 | N-Gram Language Models & Topic-Based Language Models |
| 第 5 週 | RNN Language Models & Language Understanding |
| 第 6 週 | Attention Networks & Transformers |
| 第 7 週 | BERT Encoder & GPT Decoder |
| 第 8 週 | Retrieval, Augmentation & Generation |
| 第 9 週 | Generation with Prompting Strategies |
| 第 10 週 | LLMs with GPT, LLaMA & Breeze |
| 第 11 週 | AI Computing Architecture for LLM |
| 第 12 週 | DaVinci GAI Platform & Applications |
| 第 13 週 | LLM Model Trends and Generative AI |
| 第 14 週 | Final Presentation |
| 第 15 週 | Final Presentation |
| 第 16 週 | Final Presentation |
1. Lecture Notes and Slides 2. I. Goodfellow and Y. Bengio and A. Courville, Deep Learning, The MIT Press, 2016. 3. S. Watanabe and J.-T. Chien, Bayesian Speech and Language Processing, Cambridge University Press, 2015.
- 地點
- ED708
- 時間
- PM18:00-18:30 on Monday. Appointments are required.
- 聯絡方式
- jtchien@nycu.edu.tw