Word Embeddings: Pytorch Basics and Evaluation

This lecture introduces the basics of Pytorch and word embeddings. It covers loading, manipulating, and evaluating pre-trained word embeddings. The lecture also delves into the evaluation of word embedding models through intrinsic and extrinsic tasks, including analogy-based benchmarking. Additionally, it explores the visualization and algebra of word embeddings, as well as the process of training new word embeddings. Practical exercises involve creating, normalizing, and aggregating word embeddings for downstream tasks.