115年第1學期-1038 Natural Language Processing 課程資訊
評分方式
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選課分析
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授課教師
恩達教育目標
This course bridges statistical NLP, deep neural sequence modeling, and modern Large Language Models (LLMs). The course equips students with mathematical theory and hands-on PyTorch skills to build, evaluate, and deploy production NLP systems from first principles.
Learning Outcomes:
1. Foundations: Analyze discrete text signals, linguistic ambiguity (6 tiers), and statistical power laws (Zipf, Heaps) to build robust Unicode NFKC pipelines.
2. Algorithmic Mastery: Implement core algorithms from scratch: BPE subword tokenizers, smoothed N-gram LMs, dynamic programming Viterbi POS taggers, and Shift-Reduce dependency parsers.
3. Neural Sequence Models: Build and train deep neural networks in PyTorch, including Bengio (2003) neural LMs, custom 4-gate LSTM cells, and Seq2Seq with Bahdanau attention.
4. Transformers & LLMs: Implement Multi-Head Attention from raw tensor math, fine-tune pre-trained BERT models, build Vector RAG pipelines, and execute LoRA fine-tuning.
5. Systems & Safety: Benchmark models (Perplexity, F1, BLEU), audit demographic fairness parity, and deploy multi-tier AI safety guardrails and INT8 quantization.
課程資訊
基本資料
選修課，學分數：3-0
上課時間：四/6,7,8
修課班級：資工系2
修課年級：2年級以上
選課備註：資工系國際組；全英授課
教師與教學助理
授課教師：恩達
大班TA或教學助理：尚無資料
Office HourBy appointment via email / Office: Department of Computer Science
授課大綱
授課大綱：開啟授課大綱(授課計畫表)
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參考書目
1. Primary Textbook (指定教科書 / Required Textbook)
a. Kristiani, Endah. (2026). Applied Natural Language Processing: A Hands-on Engineering Approach with PyTorch and Transformers (Course Notes & Lab Manual). Tunghai University. (Provided in PDF).
b. Jurafsky, Daniel, & Martin, James H. (2024). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (3rd ed. draft). Stanford University / Pearson.
Online Free Access: https://web.stanford.edu/~jurafsky/slp3/
2. Recommended Reference Books (參考書籍 / Reference Materials)
a. Tunstall, Lewis, von Werra, Leandro, & Wolf, Thomas. (2022).
Natural Language Processing with Transformers: Building Language Applications with Hugging Face (Revised 2nd ed.). O'Reilly Media. ISBN: 978-1098136796.
b. Eisenstein, Jacob. (2019).
Introduction to Natural Language Processing. MIT Press. ISBN: 978-0262042840.
c. Goldberg, Yoav. (2017).
Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies). Morgan & Claypool Publishers. ISBN: 978-1627052986.
d. Vaswani, Ashish, et al. (2017).
"Attention Is All You Need." Advances in Neural Information Processing Systems (NeurIPS 2017).
e. Hu, Edward J., et al. (2021).
"LoRA: Low-Rank Adaptation of Large Language Models." arXiv preprint arXiv:2106.09685.
f. Lewis, Patrick, et al. (2020).
"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks." NeurIPS 2020.
3. Online Resources & Documentation (線上學習資源與工具文件)
a. PyTorch Official Tutorials & Documentation: https://pytorch.org/tutorials/
b. Hugging Face Transformers & Datasets Documentation: https://huggingface.co/docs
c. LangChain & LlamaIndex Official Documentation: https://docs.langchain.com / d.https://docs.llamaindex.ai
e. Stanford CS224N: Natural Language Processing with Deep Learning: https://web.stanford.edu/class/cs224n/
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