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Pretraining & Finetuning
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Classroom Contents
Natural Language Processing - Fall 2024
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- 1 Final overview
- 2 SRL; Coreference resolution
- 3 Dependency parsing
- 4 Constituency parsing; CKY algorithm
- 5 HMM: Parameter estimation & inference; Viterbi
- 6 Part of Speech Tagging; Named Entity Recognition; Hidden Markov Model
- 7 Vision-and-Language LLMs
- 8 Multilingual LLMs
- 9 Abstractive summarization; Text generation evaluation
- 10 Guest lecture by Niloofar Mireshghallah: Can LLMs Keep a Secret?
- 11 Retrieval augmented generation; Extractive summarization
- 12 QA: Retrieval & Answer extraction
- 13 Parameter-efficient finetuning: (Q)LoRA
- 14 Question Answering Landscape
- 15 Guest Lecture by Tianyi Zhang: Faster & Cheaper LLMs with Weight and Key-value Cache Quantization
- 16 RLHF
- 17 Transformer types & Practical considerations
- 18 Prompting
- 19 Finetuning DeBERTa in 🤗 (demo); Midterm review
- 20 Transformer
- 21 Guest Lecture by Kylo Lo: Demystifying data curation for pretrained language models
- 22 Pretraining & Finetuning
- 23 Machine translation: Seq2seq
- 24 Machine translation: BLEU, Decoding, Attention
- 25 Language modeling
- 26 Neural classification with word embeddings; Pytorch tutorial
- 27 Vector semantics & embeddings
- 28 Neural networks foundations: Feedforward neural networks
- 29 Tokenization; Morphology
- 30 Machine learning foundations: Logistic regression
