Linear algebra and learning from data
Strang, Gilbert
Linear algebra and learning from data - xiii, 432 pages : illustrations ; 25 cm - Wellesley, MA : Wellesley-Cambridge Press, ©2019.
Deep learning and neural nets --
Preface and acknowledgements --
Part I: Highlights of linear algebra --
Part II: Computations with large matrices --
Part III: Low rank and compressed sensing --
Part IV: Special matrices --
Part V: Probability and statistics --
Part IV: Optimization --
Part VII: Learning from data --
Books on machine learning --
Eigenvalues and singular values : rank one --
Codes and algorithms for numerical linear algebra --
Counting parameters in the basic factorizations --
This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text.
9780692196380
Linear Algebras
Mathematical optimization
Mathematical statistics
512.5 / STR
Linear algebra and learning from data - xiii, 432 pages : illustrations ; 25 cm - Wellesley, MA : Wellesley-Cambridge Press, ©2019.
Deep learning and neural nets --
Preface and acknowledgements --
Part I: Highlights of linear algebra --
Part II: Computations with large matrices --
Part III: Low rank and compressed sensing --
Part IV: Special matrices --
Part V: Probability and statistics --
Part IV: Optimization --
Part VII: Learning from data --
Books on machine learning --
Eigenvalues and singular values : rank one --
Codes and algorithms for numerical linear algebra --
Counting parameters in the basic factorizations --
This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text.
9780692196380
Linear Algebras
Mathematical optimization
Mathematical statistics
512.5 / STR