Incluye índice. CONTENIDO: Deep learning and neural nets -- Preface and acknowledgements -- Highlights of Linear Algebra -- Computations with Large Matrices -- Low Rank and Compressed Sensing -- Special Matrices -- Probability and Statistics -- Optimization -- Learning from Data -- Books on machine learning -- Eigenvalues and singular values: Rank One -- Codes and algorithms for numerical linear algebra -- Counting parameters in the basic factorizations.
Resumen
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 marices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
