Stanford CS224N: NLP with Deep Learning | Spring 2024 | Lecture 1 – Intro and Word Vectors
For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai
This lecture covers:
1. The course (10 mins)
2. Human language and word meaning (15 mins)
3. Word2vec introduction (15 mins)
4. Word2vec objective function gradients (25 mins)
5. Optimization basics (5 mins)
6. Looking at word vectors (10 mins or less)
Key learning: The (astounding!) result that word meaning can be represented rather
well by a (high-dimensional) vector of real numbers
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)
For more information about Stanford’s online Artificial Intelligence programs visit: https://stanford.io/ai
This lecture covers:
1. The course (10 mins)
2. Human language and word meaning (15 mins)
3. Word2vec introduction (15 mins)
4. Word2vec objective function gradients (25 mins)
5. Optimization basics (5 mins)
6. Looking at word vectors (10 mins or less)
Key learning: The (astounding!) result that word meaning can be represented rather
well by a (high-dimensional) vector of real numbers
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)
