Cantitate/Preț
Produs

Linear Algebra and Optimization with Applications to Machine Learning

Autor Jean Gallier, Jocelyn Quaintance
en Limba Engleză Paperback – 15 ian 2020

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 59683 lei  3-5 săpt. +4528 lei  7-13 zile
  WSPC – 15 ian 2020 59683 lei  3-5 săpt. +4528 lei  7-13 zile
Hardback (2) 106561 lei  3-5 săpt. +4919 lei  7-13 zile
  WSPC – 6 mar 2020 106561 lei  3-5 săpt. +4919 lei  7-13 zile
  WSPC – 15 ian 2020 134890 lei  39-44 zile

Preț: 59683 lei

Preț vechi: 74604 lei
-20%

Puncte Express: 895

Preț estimativ în valută:
11435 12386$ 9806£

Carte disponibilă

Livrare economică 18 aprilie-02 mai
Livrare express 04-10 aprilie pentru 5527 lei

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9789811207716
ISBN-10: 9811207712
Pagini: 824
Dimensiuni: 152 x 229 x 44 mm
Greutate: 1.17 kg
Editura: WSPC

Descriere

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.