You can also watch some of 3blue1browns videos on youtube as well for a good intuition.Math's crazy though, it's really not a bag of tricks. Modern machine learning methods are described the same way, using the notations and tools drawn directly from linear algebra. It's a giant graph that all connects... a huge web. Or more like a pyramid maybe. If you want to understand PCA and such fully, you'll need a pretty solid grounding on those concepts. I know that linear algebra is essential to ML, but should one sit down and read a linear algebra book front to back or are there certain topics in LA that could just be studied?
You should probably understand basic matrix operations, determinants and matrix decompositions.Ideally you shouldn't read any textbook front to back but rather select some problems and test your knowledge as you go. I have access to Gentle's "Matrix Algebra", but have found it to be too dry and more of a reference book for a practicioner who's already studied the subject before. Throughout, the text emphasizes the conceptual connections between each application and the underlying linear algebraic techniques, thereby enabling students not only to learn how to apply the mathematical tools in routine contexts, but also to understand what is required to adapt to unusual or emerging problems.No previous knowledge of linear algebra is needed to approach this text, with … I will be taking a Linear Algebra class in the fall so I think I'll try to build a basic intuition for the subject and learned topics as I come across problems until I take the full-blown course in the Fall. Even some classical methods used in the eld, such as linear … Some of the books I'm considering are: Seber, "A Matrix Handbook for Statisticians"; Searle, "Matrix Algebra Useful … In this article, I will discuss three applications of linear algebra in three data science fields. (Although, you would learn a lot and be very well practiced at the end of it.
What's special about this book?This is the book for the recently launched OCW 18.065:This is new - first publish last month - and aimed at some core pieces of data science: linear algebra, optimization, statistics, and neural nets.I read this as two books rather than oneHad to share with someone - it's been in my cart @ ~95$ for a month or so.Not sure if this will help or not judge individual readiness, but in many Math curriculums, Linear Algebra comes right after multivariable calculus.Is this entry level? Maths books are usually there. )I can recommend Gilbert Strangs 18.06 course on MIT OCW, he's really an excellent teacher. Hell, I said the matrix multiplication was the easy part, but even that gets kind of gnarly... there's a couple ways even just to think about that.