Word Embeddings: Introduction and Applications

This lecture provides an introduction to word embeddings, explaining how they capture the meaning of words based on their context in a corpus of text. The instructor discusses the process of training word embeddings, evaluating their performance, and their applications in natural language processing tasks. The lecture covers the concept of similarity in high-dimensional vector spaces, the use of deep learning models for training word embeddings, and the importance of intrinsic and extrinsic evaluations to assess their effectiveness.