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.