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.

• 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