CS 4650 - Natural Language Processing (Georgia Tech)
Natural Language Process (Georgia Tech)
- Instructor: Wei Xu
- Lecture: Mondays, Wednesdays 2:00-3:15pm
Schedule (subject to change as the term progresses)
- Resource
- PyTorch Tutorial (w/ links to Colab notebook)
- TBA
- Course Overview
- Eisenstein 1
- TBA (to be updated)
- Machine Learning Review - linear classification
- Eisenstein 1, J+M 4
- TBA
- Machine Learning Review - logistic regression, perceptron, SVM
- Eisenstein 2.0-2.5, 4.1, 4.3-4.5, J+M B
- TBA
- Machine Learning Review - mutliclass classification
- Eisenstein 2.0-2.5, 4.1, 4.3-4.5, J+M 4
- TBA
- Neural Networks - Feedforward, optimization
- Eisenstein 2.6, 3.1-3.3, J+M 6, Goldberg 1-4, J.G. Makin - Backpropagation
- TBA
- Word Embeddings
- Eisenstein 3.3.4, 14.5, 14.6, J+M 5, Goldberg 5
- TBA
- Sequence Models - HMM, Viterbi
- Eisenstein 7.0-7.4, J+M 17, J+M A
- TBA
- Conditional Random Fields
- Eisenstein 7.5, 8.3, J+M 17
- TBA
- Recurrent Neural Networks + Neural CRFs
- Eisenstein 3.4, 7.6, Goldberg 10-11, J+M 13
- TBA
- Encoder-Decoder + Attention
- Eisenstein 18.3 - 18.5
- TBA
- Transformer
- J+M 8, Vaswani+17 Transformers, Alammar’s blog post, Rush’s tutorial
- TBA
- Convolutional Neural Networks, MT Evaluation
- Eisenstein 3.4, 7.6, Goldberg 9
- TBA
- Pretrained Language Models - part 1 (ELMo, BERT & variants, BART/T5)
- J+M 10, ELMo BERT, BART
- TBA
- Pretrained Language Models - part 2 (GPT2/3, knowledge distillation, instruction tuning)
- J+M 7, Hinton+15 Knowledge Distillation, GPT-3
- TBA
- Post-training of Language Models - part 3 (InstructGPT, preference optimization, decoding), Midterm Review
- InstructGPT
- TBA
- Open-source Language Models - part 4 (LLaMA, normalization, RoPE, AdamW, etc.)
- Llama 3
- TBA
- Open-source Language Models - part 5 (tokenization, BPE, multilinguality))
- BPE
- TBA
- In-class Midterm (close book, close note)