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Latent Semantic Indexing
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Related lectures (29)
Embedding Models: Concepts and Retrieval
Covers embedding models for document retrieval, latent semantic indexing, SVD, and topic models.
Handling Text: Document Retrieval, Classification, Sentiment Analysis
Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.
SVD: Singular Value Decomposition
Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.
Linear Algebra Review
Covers the basics of linear algebra, including matrix operations and singular value decomposition.
Vector Space Semantics (and Information Retrieval)
Explores the Vector Space model, Bag of Words, tf-idf, cosine similarity, Okapi BM25, and Precision and Recall in Information Retrieval.
Singular Value Decomposition
Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.
Singular Values: Definitions and Properties
MOOC: Linear Algebra (Part 3)
Covers the concept of singular values in linear algebra and their properties, including diagonalization and practical examples.
Singular Value Decomposition: Image Compression and Applications
Covers Singular Value Decomposition, focusing on its application in image compression and data representation.
Singular Value Decomposition: Theory and Applications
Explores Singular Value Decomposition theory, properties, uniqueness, matrix approximation, and dimensionality reduction applications.
Document Retrieval and Classification
Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.
Context and applications: Simple applications
MOOC: Introduction to optimization on smooth manifolds: first order methods
Delves into optimization on manifolds, showcasing simple applications like finding largest eigenvalues and singular values.
Singular Value Decomposition: Example
MOOC: Linear Algebra (Part 3)
Explains the step-by-step process of finding the singular value decomposition of a matrix.
Latent Semantic Indexing: Concepts and Applications
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Explores Latent Semantic Indexing, a technique for mapping documents into a concept space for retrieval and classification.
Latent Semantic Indexing
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Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.
Singular Value Decomposition: Applications and Interpretation
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Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.
Singular Value Decomposition
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Covers the Singular Value Decomposition theorem and its application in decomposing matrices.
Latent Semantic Indexing: Concepts and Applications
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Explores latent semantic indexing, vocabulary construction, document matrix creation, query transformation, and document retrieval using cosine similarity.
Singular Value Decomposition
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Covers the Singular Value Decomposition (SVD) of a matrix and its applications.
Information Retrieval Indexing: Latent Semantic Indexing
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Explores Latent Semantic Indexing in Information Retrieval, discussing algorithms, challenges in Vector Space Retrieval, and concept-focused retrieval methods.
Singular Value Decomposition: Orthogonal Vectors and Matrix Decomposition
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Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.
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