Deep Learning Applied to Natural Language Processing (NLP)
summary
Covers using deep neural networks for NLP to extract semantic information beyond tokenization and POS tools like spaCy or NLTK. Describes common model architectures with emphasis on attention-based state-of-the-art models such as BERT (Google) and GPT-2 (OpenAI) and their human-level reading comprehension capabilities.
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