線性代數

Linear Algebra and Learning from Data

+作者:

Strang

+年份:
2019 年1 版
+ISBN:
9780692196380
+書號:
MA0470H
+規格:
精裝/單色
+頁數:
448
+出版商:
Wellesley-Cambridge
定價

$

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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

Gilbert Strang has been teaching Linear Algebra at Massachusetts Institute of Technology (MIT) for over fifty years. His online lectures for MIT's OpenCourseWare have been viewed over three million times. He is a former President of the Society for Industrial and Applied Mathematics and Chair of the Joint Policy Board for Mathematics. Professor Strang is author of twelve books, including the bestselling classic Introduction to Linear Algebra (2016), now in its fifth edition.

Deep learning and neural nets
Preface and acknowledgements
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

Eigenvalues and singular values: Rank One
Codes and algorithms for numerical linear algebra
Counting parameters in the basic factorizations