Approaching (Almost) Any NLP Problem on KaggleExplore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources
Stanford CS 224N | Natural Language Processing with Deep Learning
Logistics Lectures: are on Tuesday/Thursday 4:30 PM - 5:50 PM Pacific Time in NVIDIA Auditorium. The lectures will also be livestreamed on Canvas via Panopto. Lecture videos for enrolled students: are posted on Canvas (requires login) shortly after each lecture ends. Unfortunately, it is not possible to make these videos viewable by non-enrolled students. Publicly available lecture videos and vers
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Word embeddings in 2017: Trends and future directions
Word embeddings in 2017: Trends and future directions Word embeddings are an integral part of current NLP models, but approaches that supersede the original word2vec have not been proposed. This post focuses on the deficiencies of word embeddings and how recent approaches have tried to resolve them. This post discusses the deficiencies of word embeddings and how recent approaches have tried to res
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