Linear Algebra and Learning from Data

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 matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Table of Contents

Part I : Highlights of Linear Algebra

Part II : Computations with Large Matrices

Part Ill: Low Rank and Compressed Sensing

Part IV: Special Matrices

Part V: Probability and Statistics

Part VI: Optimization

Part VII: Learning from Data

Book Description

From Gilbert Strang, the first textbook that teaches linear algebra together with deep learning and neural nets.

About the Author

Gilbert Strang has been teaching Linear Algebra at Massachusetts Institute of Technology (MIT) for over fifty years. His online lectures for MIT’s OpenCourseWare have been viewed over three million times. He is a former President of the Society for Industrial and Applied Mathematics and Chair of the Joint Policy Board for Mathematics. Professor Strang is author of twelve books, including the bestselling classic Introduction to Linear Algebra (2016), now in its fifth edition.