When I was sorting out mathematics books a few days ago, I happened to open the book "Linear Algebra and its Application" by Gilbert Strang. I flipped through it and it felt good. I also watched several episodes of MIT Linear Algebra by this professor many years ago. If I remember correctly, Gilbert Strang's Linear Algebra was previously selected as a Tsinghua textbook and is indeed a widely recognized classic of linear algebra. It is a pity that English has raised the bar, and this Chinese translation just makes up for it! If you don't like Mr. Strang. How do you evaluate the linear algebra textbook "Introduction to Linear Algebra"? Gilbert Strang's "Introduction to Linear Algebra" is the textbook for our professional linear algebra course. Compare it to any domestic textbook or... Show All Close.
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Compared with other members of the National Academy of Sciences, is Professor Gilbert Strang's scientific research achievements weaker? Is his selection as an academician more due to his outstanding impact on teaching? Show all Followers 22 In 2019, Professor Gilbert Strang of MIT released a blockbuster textbook introducing the mathematical principles of machine learning - linear algebra and learning from data. Professor Gilbert Strang received his undergraduate degree from MIT, a master's degree from the University of Cambridge, and a PhD from the University of California, Los Angeles. He then engaged in teaching and research at MIT and retired in 2023.
P16 also mentioned the relationship between these two people by the way. You should be taught how to do calculations, and the concepts are basically there. There are also answers to the questions (only the answers to odd-numbered questions), and they are really detailed! It’s all hidden in the after-school questions. The layout is very beautiful (the most pleasing to the eye I have ever seen.
This book is the Chinese translation of linear algebra and learning from data by gilbert strang in 2019. The table of contents is: Chapter 1: Key points of linear algebra Chapter 2: Computation of large-scale matrices Chapter 3: Low rank and compressed sensing Chapter 4: Special.
