Lectures related to Latent Semantic Indexing

Explores Singular Value Decomposition theory, properties, uniqueness, matrix approximation, and dimensionality reduction applications.

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Covers the Singular Value Decomposition theorem and its applications in practice.

Explores spectral and singular value decompositions of matrices.

Delves into optimization on manifolds, showcasing simple applications like finding largest eigenvalues and singular values.

Explains the step-by-step process of finding the singular value decomposition of a matrix.