Linear Algebra and Learning From Data 1st Edition by Gilbert Strang 0692196382 978-0692196380

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Product details:

ISBN 10: 0692196382

ISBN 13: 978-0692196380

Author: Gilbert Strang

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.

Linear Algebra and Learning From Data 1st Table of contents:

Part I. Highlights of Linear Algebra
Part II. Computations with Large Matrices
Part III. Low Rank and Compressed Sensing
Part IV. Special Matrices
Part V. Probability and Statistics
Part VI. Optimization
Part VII. Learning from Data: Books on machine learning

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