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Gilbert Strang

    November 27, 1934

    This American mathematician is renowned for his seminal contributions to finite element theory, the calculus of variations, wavelet analysis, and linear algebra. His dedication to mathematics education is evident through his prolific output of seven textbooks and a monograph. His lectures on introductory linear algebra and computational science are freely accessible, highlighting his commitment to broadening the reach of mathematical knowledge. His work consistently demonstrates a profound grasp of fundamental concepts and an innovative approach to tackling complex challenges.

    Wissenschaftliches Rechnen
    Computational Science and Engineering
    Calculus
    Linear Algebra and Learning from Data
    Introduction to linear algebra
    Linear Algebra for Everyone
    • 2021

      Introduction to linear algebra

      • 600 pages
      • 21 hours of reading
      4.4(69)Add rating

      Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains all the qualities of earlier editions, while at the same time seeing numerous minor improvements and major additions. The latter include: • A new chapter on singular values and singular vectors, including ways to analyze a matrix of data • A revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages • A new section on linear algebra and cryptography • A new chapter on linear algebra in probability and statistics. A dedicated and active website also offers solutions to exercises as well as new exercises from many different sources (including practice problems, exams, and development of textbook examples), plus codes in MATLAB®, Julia, and Python.

      Introduction to linear algebra
    • 2020

      Offering a fresh perspective on linear algebra, this book caters to learners of all levels, guiding them from fundamental concepts to advanced topics like singular value decomposition. Its accessible approach ensures that readers can grasp complex ideas easily, making it a valuable resource for students and enthusiasts alike.

      Linear Algebra for Everyone
    • 2019

      Linear Algebra and Learning from Data

      • 432 pages
      • 16 hours of reading

      Focusing on the intersection of linear algebra and deep learning, this textbook by Professor Gilbert Strang offers a comprehensive course that integrates essential mathematical concepts with practical applications in neural networks. It covers key topics such as the four fundamental subspaces, singular value decompositions, and optimization techniques, along with foundational elements of probability and statistics. The text is designed to be both accessible and rigorous, making it an invaluable resource for students eager to understand how linear algebra underpins modern data learning techniques.

      Linear Algebra and Learning from Data
    • 2017

      Calculus

      • 500 pages
      • 18 hours of reading

      Gilbert Strang's clear, direct style and detailed, intensive explanations make this textbook ideal as both a course companion and for self-study. Single variable and multivariable calculus are covered in depth. Key examples of the application of calculus to areas such as physics, engineering and economics are included in order to enhance students' understanding. New to the third edition is a chapter on the 'Highlights of calculus', which accompanies the popular video lectures by the author on MIT's OpenCourseWare. These can be accessed from math.mit.edu/~gs.

      Calculus
    • 2007

      Computational Science and Engineering

      • 750 pages
      • 27 hours of reading
      4.0(37)Add rating

      Covering the entire spectrum of computational science and engineering, this book delves into both modeling techniques and solution methods, including analytical and numerical approaches. It serves as a comprehensive resource for understanding the intricacies of computational methods and their applications in various scientific and engineering fields.

      Computational Science and Engineering