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Lecture 2: Word Vectors, Word Senses, and Neural Network Classifiers
1. Course organization (2 mins)
2. Finish looking at word vectors and word2vec (13 mins)
3. Can we capture the essence of word meaning more effectively by counting? (8m)
4. The GloVe model of word vectors (8 min)
5. Evaluating word vectors (14 mins)
6. Word senses (8 mins)
7. Review of classification and how neural nets differ (8 mins)
8. Introducing neural networks (14 mins)
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To follow along with the course schedule and syllabus visit: [ Ссылка ]
Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)
