Lecture
This lecture focuses on neural word embeddings, which are essential for representing natural language in machine learning models. The instructor begins with a recap of the foundational concept that words can be represented as vectors. The lecture then delves into various methods for creating dense vector representations, including Continuous Bag of Words (CBOW) and Skip-gram models. The instructor discusses the challenges of using sparse word representations and the need for a vocabulary that can effectively capture the nuances of language. The goal of embeddings is to learn semantic relationships between words, allowing for better performance in natural language processing tasks. The instructor explains how to train these embeddings using self-supervised learning techniques, emphasizing the importance of context in understanding word meanings. The lecture concludes with a comparison of CBOW and Skip-gram methods, highlighting their differences and applications in learning word representations. Additional resources for further exploration of word embeddings are also provided.