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This lecture introduces deep learning for NLP through CNNs, RNNs, and especially transformer language models. It covers decoding and text generation, sequence-to-sequence models, back-translation, word2vec, GPT, BERT, and pre-training.
Syllabus
– Week 12 – Lecture
– Introduction to deep learning in NLP and language models
– Transformer language model structure and intuition
– Some tricks and facts of Transformer Language Models and decoding Language Models
– Beam Search, Sampling and Text Generation
– Back-translation, word2vec and BERT's
– Pre-training for NLP and Next Steps
Taught by
Alfredo Canziani