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Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special marices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

The first textbook designed to teach linear algebra as a tool for deep learning
From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra
Includes the necessary background from statistics and optimization
Explains stochastic gradient descent, the key algorithim of deep learning, in detail



Table of Contents
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 VI. 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
Index of authors
Index
Index of symbols.