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This is my favorite introductory textbook on linear algebra which goes to great lengths to avoid matrix and determinant computations: http://www.amazon.com/Lin

• nashequilibrium 13y ago
  Thanks, i have been doing a lot of machine learning hacks lately and almost everything involves "vector spaces", 249 pages should be fun, just finishing up "Think Bayes" by Alan Downey.
• clebio 13y ago
  I read Axler's book quite recently, after pouring through reviews of linear algebra books. I did like it quite a lot -- it has a very clean, approachable narrative style. Having completed it, though, I feel there's maybe not enough operational knowledge gained from it. Meaning, it explains the theory, the motivations, well, but one might augment it with Schaum's[1], or some such guided exercises. [1]:http://www.amazon.com/Schaums-Outline-Algebra-Edition-Outlines/dp/0071794565/ http://www.amazon.com/Schaums-Outline-Algebra-Edition-Outlin...
• 11001 13y ago
  Axler is not a text in applied linear algebra, it is a book you should read after you are already familiar with both basic abstract algebra and basic linear algebra (i.e. you know how to multiply matrices etc.), it then provides a nice motivation for linear algebra as a subfield of abstract algebra. You may still, however, not find the ideas of determinant, trace and other practical concepts very intuitive after reading Axler.
  • j2kun 13y ago
    Basic abstract algebra? Definitely not needed to use that book to its full potential.