Linear Algebra and Learning from Data (inbunden)
Inbunden (Hardback)
Antal sidor
Wellesley-Cambridge Press
Worked examples or Exercises
Worked examples or Exercises
239 x 196 x 24 mm
920 g
Antal komponenter

Linear Algebra and Learning from Data

(1 röst)
Inbunden,  Engelska, 2019-01-31
Billigast på PriceRunner
  • Skickas från oss inom 2-3 vardagar.
  • Fri frakt över 249 kr för privatkunder i Sverige.
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.
Visa hela texten

Passar bra ihop

  1. Linear Algebra and Learning from Data
  2. +
  3. The Anxious Generation

De som köpt den här boken har ofta också köpt The Anxious Generation av Jonathan Haidt (inbunden).

Köp båda 2 för 1123 kr


Har du läst boken? Sätt ditt betyg »

Fler böcker av Gilbert Strang

Övrig information

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; Index of authors; Index; Index of symbols.