Linear Algebra for Data Science, Machine Learning, and Signal Processing
portes grátis
Linear Algebra for Data Science, Machine Learning, and Signal Processing
Fessler, Jeffrey A.; Nadakuditi, Raj Rao
Cambridge University Press
05/2024
450
Dura
Inglês
9781009418140
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1. Getting started; 2. Introduction to Matrices; 3. Matrix factorization: eigendecomposition and SVD; 4. Subspaces, rank and nearest-subspace classification; 5. Linear least-squares regression and binary classification; 6. Norms and Procrustes problems; 7. Low-rank approximation and multidimensional scaling; 8. Special matrices, Markov chains and PageRank; 9. Optimization basics and logistic regression; 10. Matrix completion and recommender systems; 11. Neural network models; 12. Random matrix theory, signal+ noise matrices, and phase transitions.
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1. Getting started; 2. Introduction to Matrices; 3. Matrix factorization: eigendecomposition and SVD; 4. Subspaces, rank and nearest-subspace classification; 5. Linear least-squares regression and binary classification; 6. Norms and Procrustes problems; 7. Low-rank approximation and multidimensional scaling; 8. Special matrices, Markov chains and PageRank; 9. Optimization basics and logistic regression; 10. Matrix completion and recommender systems; 11. Neural network models; 12. Random matrix theory, signal+ noise matrices, and phase transitions.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
