Linear Algebra and Optimization for Machine Learning: A Textbook

by Charu C. Aggarwal

Preface.- 1 Linear Algebra and An Introduction.- 2 Linear Transformations and Linear Systems.- 3 Eigenvectors and Diagonalizable Matrices.- 4 Optimization A Machine Learning View.- 5 Advanced Optimization Solutions.- 6 Constrained Optimization and Duality.- 7 Singular Value Decomposition.- 8 Matrix Factorization.- 9 The Linear Algebra of Similarity.- 10 The Linear Algebra of Graphs.- 11 Optimization in Computational Graphs.- Index.

Genres: Mathematics, Artificial Intelligence, Technical, Programming

520 pages, Paperback

Published May 18, 2020

Reviews

[Andrew Hamara · Rating 2 out of 5]

An okay book, but the writing is very dry and honestly not that good. There are lots of proofs and equations that show up with little or no explanation as to why they are relevant to the content of the chapter. They just kind of get vomited on the page. So if you skim through the pages of this book, it will look really advanced but it's just a bunch of disparate, unexplained stuff. The book is probably most useful as a reference if you already know the subject and want to revisit topics.

If you're interested in learning this subject, I would recommend "Mathematics for Machine Learning" by Faisal. You will get a lot more intuition out of that book, which is a lot more valuable to learning in my opinion.