Latent Semantic Indexing: Concepts and Applications

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Related lectures (28)

Page 2 of 3

Introduces the basics of information retrieval, covering document representation, query expansion, and TF-IDF for document ranking.

Unsupervised Learning: Movie Recommendation

Covers unsupervised learning for movie recommendation using singular value decomposition.

Latent semantic indexing: inverted files

Explores term-offset indices in inverted files and relevance feedback solutions.

Introduction to Information Retrieval

Introduces the basics of information retrieval, covering text-based retrieval, document features, similarity functions, and the difference between Boolean and ranked retrieval.

Singular Value Decomposition

Explores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.

Singular Value Decomposition: Applications and Interpretation

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Text Handling: Matrix, Documents, Topics

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Deep Generative Models

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

SVD: Singular Value Decomposition

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

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