Linear Algebra and Learning from Data

For over fifty years, Gilbert Strang has taught Linear Algebra at the Massachusetts Institute of Technology (MIT). His MIT OpenCourseWare online lectures have been viewed over three million times. He has previously served as President of the Society for Industrial and Applied Mathematics and as Chair of the Joint Policy Board for Mathematics. Professor Strang has written twelve books, including the best-selling Introduction to Linear Algebra (2016), which is now in its fifth edition.

Finally, linear algebra and the fundamentals of deep learning come together! Linear Algebra and Learning from Data, the first textbook that teaches linear algebra with deep learning and neural nets, is written by Professor Gilbert Strang, the famed author of Introduction to Linear Algebra. This readable yet rigorous textbook provides a comprehensive course in linear algebra and associated mathematics that students will need to understand learning from data. Singular value decompositions, special matrices, huge matrix computation techniques, compressed sensing, probability and statistics, optimization, neural net design, stochastic gradient descent, and backpropagation are all covered.

Nowadays, computers can learn a lot from digital photographs or movies. From an engineering standpoint, it tries to comprehend and automate operations that the human visual system is capable of performing. The book describes how computer vision works and replaces the human eye in many areas.

Author: Gilbert Strang

Link to buy: https://www.amazon.com/Linear-Algebra-Learning-Gilbert-Strang/dp/0692196382/

Ratings: 4.6 out of 5 stars (from 202 reviews)

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