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.

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.

"About this title" may belong to another edition of this title.

Store Description

BooksRun helps to save money on textbooks. We are committed to providing each customer with the highest standard of customer service. Please pay close attention to the description of the book and its condition you are ordering. If you have any questions or issues, please make sure to contact us first so we could resolve them. All orders are shipped on the same or the next business day. It usually takes 1 to 5 business days to receive your item if you choose expedited shipping. All orders submitted are eligible for a return within 30 days after the delivery. Thanks for shopping with us!

Visit Seller's Storefront