ELL884: Difference between revisions
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| credits = 3 | | credits = 3 | ||
| credit_structure = 3-0-0 | | credit_structure = 3-0-0 | ||
| pre_requisites = ELL409/ ELL784/ COL774 | | pre_requisites = [[ELL409]]/ [[ELL784]]/ [[COL774]] | ||
| overlaps = COL772 | | overlaps = [[COL772]] | ||
}} | }} | ||
== ELL884 : Deep Learning for Natural Language processing == | == ELL884 : Deep Learning for Natural Language processing == | ||
Introduction to classical NLP, tokenization, N-gram language models and their evaluation, POS tagging (HMM, Viterbi, MEMM), Parsing (CKY parser), lexical and distributional semantics, introduction to deep learning (ANN, backpropagation, activation functions, RNNs, CNNs), RNN based language models, sequence-to-sequence models, attention and self-attention, transformer, advanced transformer architectures (BERT, RoBERTa, GPT), prompt-based learning, integrating knowledge in language models, retrieval-augmented models, editing neural networks, multilingual NLP, multimodal NLP, other applications of NLP, ethics in NLP, LLM and creativity. Courses of Study 2024-2025 Electrical Engineering 194 | Introduction to classical NLP, tokenization, N-gram language models and their evaluation, POS tagging (HMM, Viterbi, MEMM), Parsing (CKY parser), lexical and distributional semantics, introduction to deep learning (ANN, backpropagation, activation functions, RNNs, CNNs), RNN based language models, sequence-to-sequence models, attention and self-attention, transformer, advanced transformer architectures (BERT, RoBERTa, GPT), prompt-based learning, integrating knowledge in language models, retrieval-augmented models, editing neural networks, multilingual NLP, multimodal NLP, other applications of NLP, ethics in NLP, LLM and creativity. Courses of Study 2024-2025 Electrical Engineering 194 | ||
Latest revision as of 16:32, 14 April 2026
| ELL884 | |
|---|---|
| Deep Learning for Natural Language processing | |
| Credits | 3 |
| Structure | 3-0-0 |
| Pre-requisites | ELL409/ ELL784/ COL774 |
| Overlaps | COL772 |
ELL884 : Deep Learning for Natural Language processing
Introduction to classical NLP, tokenization, N-gram language models and their evaluation, POS tagging (HMM, Viterbi, MEMM), Parsing (CKY parser), lexical and distributional semantics, introduction to deep learning (ANN, backpropagation, activation functions, RNNs, CNNs), RNN based language models, sequence-to-sequence models, attention and self-attention, transformer, advanced transformer architectures (BERT, RoBERTa, GPT), prompt-based learning, integrating knowledge in language models, retrieval-augmented models, editing neural networks, multilingual NLP, multimodal NLP, other applications of NLP, ethics in NLP, LLM and creativity. Courses of Study 2024-2025 Electrical Engineering 194