linear algebra crash course

Linear algebra (Linear Algebra) This discipline everyone is not unfamiliar, if someone still feel a little rusty, then "determinant", "matrix" These concepts you should always have the impression. Each major of the university will study this course in different shades, the l

Linear algebra is the basis of machine learning, as early as six months ago we have developed linear algebra learning, of course, for machine learning, linear algebra is not to achieve

also used by many people for engineering development. On top of Python, there is a well-known mathematical computing library, numpy and scipy, which has also been transplanted to. Net (the porting process is with the help of Microsoft ). To use these two libraries, use Python tools for Visual Studio-an integrated Python Development Environment implemented in vs2010. The specific use of the wizard can access: http://pytools.codeplex.com/wikipage? Title = numpy % 20and % 20 scipy % 20for % w.

also a book, "Linear algebra should be learned", to see the watercress, it is a good reference in linear algebra. This article, to learn to think good linear algebra of the students of the road signs. ==========

also used by many people for engineering development. On top of Python is a well-known mathematical library-NumPy and SciPy, which is also ported to. NET (the porting process is with Microsoft's help).The best way to use these two libraries is through python Tools for Visual Studio-A Python integrated development environment implemented in VS2010.Specific usage wizards can be accessed by:Http://pytools.codeplex.com/wikipage?title=NumPy%20and%20SciPy%20for%20.Net3, development using C #In. NET 4

inner product can also be applied naturally to any dimension of space.With the concept of distance, of course we have to do something interesting with it. or from the concept of geometry, we can use distance to calculate the perimeter and area of a plane graph and the volume of the spatial graph. For example, we can calculate the coordinates of a point in a line or a plane that is closest to the nearest point on the straight or flat plane. The two ap

This paper is Gilbert Strang's introductory course note on linear algebra. Course Address: http://v.163.com/special/opencourse/daishu.html 24th class: Markov Matrix, Fourier seriesThis lecture explains the application of eigenvalue, Markov matrix theory, Fourier series (it is the ingenious application of projection mat

??Recently in the implementation of the algorithm, the feeling of lack of knowledge of mathematics, here -------------------------------------------------- calculus --------------------------------------------- ------------- Calculus Open Class: mit: univariate calculus http://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/ Http://v.163.com/special/sp/singlevariablecalculus.html mit: Many variable Calculus Http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-c

mathematical knowledge related to machine learning and machine vision. This was a web site I found to introduce the relevant mathematical knowledge: http://bbs.sciencenet.cn/home.php? Mod = space uid = 240720 do = blog id = 284694 many classic books are mentioned above, but it does not seem to have this linear algebra book. It is another MIT teaching material, there are related open

To pick up the flowers Don't forget beginner's mind Apply 've seen Bit by bit Linear algebra is a compulsory course in the University of Science, I am ashamed to say that I have not learned, throughout the semester all the classes I was thinking about one thing: this thing is very complex, what can be used to do? Think back to the math lesson

Linear algebra is the basis of machine learning, as early as six months ago we carried out linear algebra learning, of course, for machine learning, linear algebra is not the degree of

has another well-structured similarity matrix, which makes the elementary transformation very convenient. It makes a discussion of the similarity matrix of the \ (a\), which is equivalent to the discussion of the similarity matrix of the \ (b=\begin{bmatrix}\lambda_0\\\bar{\lambda}_0\end{bmatrix}\).\[\begin{bmatrix}1\\\lambda^{-1}\\\ddots\\\lambda^ {-(n-1)}\end{bmatrix}\begin{bmatrix}\lambda1\\\ddots\ddots\\\ddots 1\\\lambda\end{bmatrix}\begin{bmatrix}1\\\lambda\\ \ddots\\\lambda^{(n-1)}\end{bm

PCA Dimension Reduction-- Minimum Variance interpretation (linear algebra see PCA) Note: According to the online data collation, welcome to discussThe complexity of the machine learning algorithm is closely related to the dimensionality of the data, even with the dimension number of the exponential association. So we have to dimensionality the data.dimensionality, of c