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Natural Language Processing with Deep Learning: Introduction & Word Vectors (CS224N)

Christopher ManningDistinguishedStanford University

Thomas M. Siebel Professor in Machine Learning

1:24:27·2021·graduate level·Standalone Lecture

This lecture introduces foundational concepts in Natural Language Processing, examining how human language and word meaning can be represented by dense vector embeddings. It provides a detailed introduction to the Word2vec algorithm, covering its objective function, gradients, and optimization basics.

AI & MLLanguage Models & NLPAlgorithms & Data StructuresDeep LearningNatural Language Processing

This lecture covers: 1. The course (10min) 2. Human language and word meaning (15 min) 3. Word2vec algorithm introduction (15 min) 4. Word2vec objective function gradients (25 min) 5. Optimization basics (5min) 6. Looking at word vectors (10 min or less)

Source: Stanford Online via YouTubeWatch on YouTube
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Christopher ManningDistinguished

Thomas M. Siebel Professor in Machine Learning & Director of SAIL, Stanford

Director of the Stanford AI Lab (SAIL) and pioneer of deep learning for natural language processing (CS224N, GloVe, attention mechanisms).

2 lecturesCanon

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