Stanford CS224N: NLP with Deep Learning | Spring 2024 | Lecture 2 - Word Vectors and Language Models

For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai This lecture covers: 1. Course organization (3 mins) 2. Optimization basics (5 mins) 3. Review of word2vec and looking at word vectors (12 mins) 4. More on word2vec (8 mins) 5. Can we capture the essence of word meaning more effectively by counting? (12m) 6. Evaluating word vectors (10 mins) 7. Word senses (10 mins) 8. Review of classification and how neural nets differ (10 mins) 9. Introducing neural networks (10 mins) To learn more about enrolling in this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning To follow along with the course schedule and syllabus visit: hhttps://web.stanford.edu/class/archive/cs/cs224n/cs224n.1246/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL)